{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# High-Dimensional Time Series Forecasting with Convolutional Neural Networks\n",
    "\n",
    "**Note**: for a written overview on this topic, check out my [blog post](https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_conv/). \n",
    "\n",
    "This notebook aims to demonstrate in python/keras code how a **convolutional** sequence-to-sequence neural network can be built for the purpose of high-dimensional time series forecasting. For an introduction to neural network forecasting with an LSTM architecture, check out the [first notebook in this series](https://github.com/JEddy92/TimeSeries_Seq2Seq/blob/master/notebooks/TS_Seq2Seq_Intro.ipynb). I assume working familiarity with 1-dimensional convolutions, and recommend checking out [Chris Olah's blog post](http://colah.github.io/posts/2014-07-Understanding-Convolutions/) if you want a nice primer.  \n",
    "\n",
    "In this notebook I'll be using the daily wikipedia web page traffic dataset again, available [here on Kaggle](https://www.kaggle.com/c/web-traffic-time-series-forecasting/data). The corresponding competition called for forecasting 60 days into the future, but for this demonstration we'll simplify to forecasting only 14 days. However, we will use all of the series history available in \"train_1.csv\" for the encoding stage of the model. \n",
    "\n",
    "Our goal here is to show a relatively simple implementation of the core convolutional seq2seq architecture that can be nicely applied to this problem. In particular, I'll use a stack of **1-dimensional causal convolutions with exponentially increasing dilation rates**, as in the [WaveNet model](https://arxiv.org/pdf/1609.03499.pdf). Don't worry, I'll explain what all that means in section 3! Feel free to skip ahead to that section if you're comfortable with the data setup and formatting (it's the same as in the previous notebook), and want to get right into the neural network.     \n",
    "\n",
    "Here's a section breakdown of this notebook -- enjoy!\n",
    "\n",
    "**1. Loading and Previewing the Data**   \n",
    "**2. Formatting the Data for Modeling**  \n",
    "**3. Building the Model - Training Architecture**  \n",
    "**4. Building the Model - Inference Loop**  \n",
    "**5. Generating and Plotting Predictions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading and Previewing the Data \n",
    "\n",
    "First thing's first, let's load up the data and get a quick feel for it (reminder that the dataset is available [here](https://www.kaggle.com/c/web-traffic-time-series-forecasting/data)). \n",
    "\n",
    "Note that there are a good number of NaN values in the data that don't disambiguate missing from zero. For the sake of simplicity in this tutorial, we'll naively fill these with 0 later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2015-07-08</th>\n",
       "      <th>2015-07-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-12-22</th>\n",
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       "      <th>2016-12-24</th>\n",
       "      <th>2016-12-25</th>\n",
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       "                                                Page  2015-07-01  2015-07-02  \\\n",
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       "3         4minute_zh.wikipedia.org_all-access_spider        35.0        13.0   \n",
       "4  52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...         NaN         NaN   \n",
       "\n",
       "   2015-07-03  2015-07-04  2015-07-05  2015-07-06  2015-07-07  2015-07-08  \\\n",
       "0         5.0        13.0        14.0         9.0         9.0        22.0   \n",
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       "3        10.0        94.0         4.0        26.0        14.0         9.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   2015-07-09     ...      2016-12-22  2016-12-23  2016-12-24  2016-12-25  \\\n",
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       "4         NaN     ...            48.0         9.0        25.0        13.0   \n",
       "\n",
       "   2016-12-26  2016-12-27  2016-12-28  2016-12-29  2016-12-30  2016-12-31  \n",
       "0        14.0        20.0        22.0        19.0        18.0        20.0  \n",
       "1         9.0        30.0        52.0        45.0        26.0        20.0  \n",
       "2         4.0         4.0         6.0         3.0         4.0        17.0  \n",
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       "4         3.0        11.0        27.0        13.0        36.0        10.0  \n",
       "\n",
       "[5 rows x 551 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "df = pd.read_csv('../data/train_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145063 entries, 0 to 145062\n",
      "Columns: 551 entries, Page to 2016-12-31\n",
      "dtypes: float64(550), object(1)\n",
      "memory usage: 609.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data ranges from 2015-07-01 to 2016-12-31\n"
     ]
    }
   ],
   "source": [
    "data_start_date = df.columns[1]\n",
    "data_end_date = df.columns[-1]\n",
    "print('Data ranges from %s to %s' % (data_start_date, data_end_date))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define a function that lets us visualize some random webpage series as below. For the sake of smoothing out the scale of traffic across different series, we apply a log1p transformation before plotting - i.e. take $\\log(1+x)$ for each value $x$ in a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T2yVx69Yt6tSpU2z5RWFubk5GRgYXL14kPDwcb29vufEaN26Mnp4eFy9e5NSpUwwePLhQ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QKhtF+f+9+h0TQAV6euj8XQZKXh7Vmbv1+Y8fFtFXr5SD5qNvnT0zMcX63wlEc2DEQVBlSFhUYL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\nwKzHzYeDp6b4zPf388G7ttVsfV4sBsfTNNcHUGSJI54wsXVL464MfCFw/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pojg0meOjzG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\nB3Hd4J3I1Sfx1MMnMDt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PqDGuftN6XPeWjVizsb3l+wsFYz2lbHCMDUxKI3AnJgOiCcqs\nIq6rIoTQ9F/Ej0np6l7WsUUjfellUHv7uIbPnhjH3A9/ABDCXdLl/NLAw6sRcls7nJlpKD29WPHR\nj71q38tAeHd/ZsnVoOcVqJIjTNXq//Wn6PnVXwMAiFqC35yM2UjvuDzUKsgS4Lp8UFb7+gFBiJkB\nNkZUm5TOJlB+5mlMff3fYoMuCzsAZ0pPL2TfgqmkAQgxAZtvGMhYdAAbHymBkDgjpWoyX4G3AlWt\nQpJEdPdlMDNR4YJsb/A4yrueg6xI0BJyrALQrDsgJG6Kl9GBjTMvoL02CtF3IQgCNTUzXV695Agy\nbMtD/+o8JMmDIIBX/wFAXc/g/vH+Jm8nNni3d6UwOVamx+GHoGp0uICJUfp71aphWhag2gMpnWkJ\n1nzLQn3gOBd+N0aMqWqhqXKLRdRPnaLvBwBsNhV0Kg+YKnaNGhte33/XQYwMFTAzVYUXpGNdz4dX\nM0Ijukj7JDdSUcMmWQYSpNwiQNXJAXhBSifWY9B1AVniuoompqpahZhKQUqlWhpKLid8n8Q0fCwK\nMwZ8nzTZhiwUDMgIisJbILUKDk6CRRFLk9cNG6VCnVfoxj5DCLdOAMCrJJXOTkxPGRhsuwSCpnFQ\nxUrGo6mZ+XyEGPjwPR/m8BAghJVdIgdViVhBQjS8ahVikm537CAdO7777y/hpQOtmSWSC8e9upqD\nQDwknPA3ZYO6dyn1G0okFaxYncfRAxP4ztf34LF7j4IQArPuYHykhFKhjnWbO5FMqxg4HC4szbqD\n9Vs6seOa1Vi7iX4n+72jTI6WkPGmq7JYUT4OmdD0KgQRxHE5eI2m/6QkBUuiKGDjBRQACCIgyRlY\nSMAmEgYG1uHk4CoQnzJVeiJYgEj0t85oPmQFEEQJkixCkDQ4gbO7rNLzF1wVQkqKaZLmCymbhWyW\noShicH6tQZUsS7j48pUQl8mAsDYyUlCxKDWYUzaymYypEhMJrheOVvL69Trg+5BSKaz8wz9Gzwc+\nuCR2xrHmUBx7tImBF2QZ3e//daj9/Uhs3ARj70soPHg/cjfehHTgjH6+MVVACDi73vMLkF8FvReL\ndEbDO395O975y9uX/NnzAlTx6r9IuaW2ahVvPyFls1xcyppNRk3ABFmBHwFVop6E3N7ekqkyDh3E\n+L9+CSv7wkEkk9U4mGqVjmJtJJJbtkKOmGGy9B89BwMJ14Ak+JgMgEQUPGkNAGux0daRRGG2xkWm\nxv13Y+LWLwGg7sGHXhrDvr//MoDQD0tPyJx2Jp6PNcVDuGzsoZBOTsiwLIczVaZHj2flmjx0nQ4C\nkqhBEOlAVEu3o+arODMUr1QxAqC0bnMn6oaDSskMq/9cE9//5j48+eBAsG0wiAYTmcCZqjio8mo1\n2lMt4l0WjcN7x/DCU0N0H0KzUJ0QgrEv/RNG/u7T8E0TZt1Bxg7309mT4dcOAGZfClvdGIcP8aqq\nyoGDzDcVviChuvvF8BgjKT23oYkvEIAEUaTndxahOvE8OFNT8FsxVa4HQZLDfmIRUEV8H369RtN/\nmUyTxmu5cfrkLO65cx9v28SC/c5RMMuO96G7D+PwvrGWaVR7YhwQBCQv2sZbIAH0OsfczyPn5js2\nX3w4todv/Mvz+Oat8XQrQItVzvzNX8XSfwBlu0ezW3CqYwdcSYWUy0HQErwvYOz3irAIbrGAsS9+\nAV6tBjNIKbuuD2t4CGpfP13lI5w0BU2dF5R5RhVIh8UxruuhWrZQKDZbMAiKApKLN+2ViIekFgKH\nju4UBAHcX0pRJVzz5o3cWw+gGs6nHz6B739jLypFE/n2JDZd0I3TJ+cwOVZGzbBhmS7aOlJ4ww3r\n0d5J98mYrGj6D6Cl9amLqcknAqdz4rncZZum/2oUcGrhZy+4NJA6FE1oSQ2epMKREigXV6BcbYPr\nSlBkF7oeyDkE+luTeg25vAo9SfclihqYb5Sk0mspeCIETcJiPB719RshAMgk6D7mA1WLCd81cWbf\n38Asn2p6j4F0Zv3DwDT/bANTxcZlMaFzENPKc05MpZDcshW5a69f0rHWCodQnnwKntM8jyU3b8Ha\nT/0Vklu30u/QNHS95xc5E9aq19+PO3p+/TeQu/GNSF508cIbv8zRvzp/1grT+eK8AFWuYVD9SAtb\nfICCLdbmgTEbUaMvQZZBnFAjJKgK1O6elpqqyvPPofL8c5j8h8/yBU86m+CrhVZNcO2JcYjpNNSV\nK6FEzDATugzi+yg8/BDcYhECgJRo48wpOonnO0IqWI0CrCUI39o6U3Bsj4uqFc/iq2aW2nrO2oCx\nM0XuQ1N/9Ic48ZEP05V8QPcKisL/1hKUqWITdc2lt0E+O4Qbr6VNLIkLiDL9Pbyg7QOzcmDB+oKt\nCwb3qfEKT/9RTVU40VJzUwsO02EkElQPVK1yLRLxfZz+y/+D0c99BgAtfW80bjyyb5z370pntCZQ\nZezdA/PEAIhtwzi4H/WajVxtEtcN3ombuiYgGiWUnngcesBUjf/wAUydHMfd39iLPU8P8f1MP/AQ\nXDsAiIKE2tEj/D0/ApQYGBeTSV7159s2TW/qehNTVXnxhXCCn5sDfB+eTK9vXFPlxKv/IkJ13zAo\nbZ9KQ+nsglcuh+lx225pKLiYqAW+RYxV5K/PA6pGBgs4cWQKj993HEf2N6fqnIlxKJ2dUHupqzi1\nEahj9HOfwejf/114rhHGxzVtXrBhW+FvO/i122P3gnn6NMzBUzz1yRgUub2D65IMT6XMbE8PT0Uy\n9kzKZmMTXu3IYUzvO4r68WP8nnIdD+bpYSTWrIXc0QECYFhaCdtyafrPtkBcF6P/+HlUjoWaMc8w\nQFLh+DQxQscUxuwe7boaJ9p3gADo/8jvIfXmmwEAKS+oGhQI0gHIUTUJsiwhk0vw9KuiSMi3J/GW\nd1yIN7+NTpCFWQMnj07B9wl8nyDXpmP7ztXQUyq+8/U9uO0fafqVseuyIiHbpmN8hIKCqJM0QMHj\nit/7A4jpNAjxIQgCiONCTDAzWhd+rQ5R12Pl9129GVx+zRrccPNmJDJ027qcRiKj024OporO/By6\nRHa/eIBI5wBNLSPbQSd4NRm2AJKVgKHwAiNof+HK0MSGDVDb26HX6biemif9t5hwnTKIb8GxmtlW\ntqBhoEpqSv/Fn3/O7uk6BFmGlMnGFo/RCtPlhBeYLntnMV9mYC7zhp0QE4kQVJ2HTFXm8ivR86u/\n9opZPLwScV6AqurJUzwX3fP+D2D1n/x57H25vR3O3ByI70dKKxtAlRsyL6KiQm5rj3VA5xGsqK3T\nw3jbTh07b1qPhK7EmCpzeCi2CrbHx6D29kHO5WNMVWdPBvXjxzB95x2oBlUVacHkVWOd3eFAFWWq\npv/lC6ju37uoa9MWALPxM/T4FM/imoJrr+nFjpEfQncquP/bBzA0EADPw1SMaA4N8XRcK1DFQGg9\nGKMkEmozHr1/EG7Qld7XKQBhhqksjKqNZFpDWyc9xsKMEen5RyBJ4SRYLVu47QvP4rkX6TE+vKuC\ne051YDy1lg80tcOHeDUcPRGviTlkpn0AkMnrTdV/1b17IaUzkDIZlHfvhm15UDwTmmci58xi8I8+\nhsmvfw0a6MTpSAmMDs5g7HQRB2fD38sVFXjB4+EJEmpHjwKCQFN6tQamSpIgt3eETJVtQVBViHoy\nJlQnvo/xf/0Sio88BACwA9Av9VKDSNfxOePTWP3nRywVPG6El+ICVLaAmLnrWxj+8z/lXQmWEnaQ\nKm3UqTGWkXmD2VNTIITg9KlZKKqEXJuOYweaWWF7YhxKTx+Uzk4Q14VbLMA4eCA4nwhItG3YUgKu\nqMCuRZyuK+G1O71/CIX7fxheg3IJ8H3YQYqYMVVSvg2GSicHw6XPnNrby9N/9RMDUPv76e8VAVWD\nQxU8s/bdmDo5zjVVdsWAVypBW7MWSns7qmob9td6ceLoFEQtAc+ysOeJAUwfPoXCS+Hz7BtVIBlO\niqxIo1IyseYvPo3R3BYMt1+C4baLoW/cBC9Bt+306eSqpnR0veOdSKZUKCo9h1ybzlPViho1wKT3\nB7WAIBADkXmuTUcypeK//cLFvNoZQMzIkOmquvsy83ozSckU6OIoYKqYw3+Q/mP/ZyEIAq66YR16\nV+SQyNBxoa5koOdSeNsvXAK9oCOVtdC/OlKEoIhw65PwXQOJDLXFyHRdFe4zSI8LAagi/sL+T4Io\nouPqnVBn6P2RzixsmzNfsKbyrWxi3FKJjgvBfNTYm66RzYwyVQBlh6zRUf7c82d7maDKd4zg3/kl\nAZmrdqLt5p9F13toVaq+YQNNC67fsKzvfD3icV6AqtK+/RxU5W64sak7ttLRQSfYcnlepgqex31u\nBFWBqOtNqwSAGu+pK1ZC6eqG//SDuGwnraxgq3uvXEH95AnUB47z9KE9MQ61tw+pbRcje8UVAICs\nOY2E5IdsWPBQpDx6U+fbdT4gAiFTpWoSagf2cbO1hYKDqpESBOJDIg6IZdHqpqkRtJlT2D72EBRF\nwME91A8r3UnTpsbePbyZrKAokfSfQhs922VIl2RRNX1q1KeH13RmzkNxMpjUknTCKswYMMYmUJyr\n4Xu3P46x4QJSGZWvpGmj5pBtkWUXsiKif1WOp3TOTNhwRA1jkxZMV8CcvgLDn/ozFH/0KEpPPs6r\ntVjETC49n7NxANDRlcL0JPUaM4eGUNn9Iq2Ky+WQunQ7ikcCq4PA3sEIKl8AQLIMCCCwpQTMKr1P\n3jD3GK4/9U36vZLKm776ogyvVISUzUHOZuNNv0tFyNlcTLjsW7RJtahTLSBjWLxKJQYUWb9LsStc\nlfOejJ4LQZJ49R9bMEwMPobSzBP0HNJpqIETtzNNgU51z4vwqhXUAlG97zhwFmikymI+l3p2zT2n\nhJF9n8XwZz6JwoMP4vSpOaxc04YtF/diYqQU09wR34c9OQm1t5f7zTgzM6i+RNtOaKtWobL7RZhD\nQyCWjX19b8axrp2wA08wzTXgQeIl73PJfpSeeDy8TkHaxRweotcwSEEZUgZ+4PVUtQLfpZ5ejNcS\n+PZXd6F6agjC+q0YUDZgzgyfzxMBJhwZNThTZs7Q6+Z0r8LDUyt4i5RyoQ5R02CJOnbtmsSza27B\n7GSkCW7VANHD+/j0Sbof1/HhZ9vBCs0Keh8EVeW6v54kPXc1rSO9/TKkMhoXfjNWGkBsXGGmhWcG\nC0ilVawKil1yQSP4jq40fi4wPwYQ89zpCNipVevnF2mLyWSgqRJA3Hj6z6vXeOVfq0jkAoAhCEil\nNWTzOhJmG0jVhZiNuKGrIiyHgl4GqiQljWzPdUh3XhFuF/h2+e7iGqmnNqxDR+U0urt15Nv14LMm\nypNPw3dNlCaeAiEeXLuE8aO3wrVbp9GZPUwrUOWVS9RTKug5y5gqBq4aDUBDTSm9jtlrroV58gSG\n//QTmLrzG7H033LCc6vBv/MzVVIyia53/0II7PJtWPupv+Ktkl6Pc4vzAlQBzbRpNOSgSqL4+GOo\n7tkNMZHgJawAIKoqLZVm1WwK7ZPlm2aT1sOvUS1KZufVME+dpC1yCIEXMFVuucSZCLdchletwqtU\noPb1QdQ0bHrbTcgngQsnn4JXM/jEyCLpBe1mejMghKD4+GNwy+WwKiZYZbbSC7UKPaXSqjzHh0Js\nToN65TLMIZrj190qrt4arjSloJVFdd9eXu0hKmoDU+WgLh6Hcn0nfDKBVEYD8SIMjCujVgiuZ1KB\n6HsgBNj/6X/A3seewY5tj0ORJnlDyraOJIqz9QhTBSiyh1/6rauweVvoCKyrBIYa6k1MOQl3dhZT\n//511I8fR/rSy7D+s/8XKz/28eA6haDKaPDdueTKlQAB9jx3GnP334sT//F9jFY1jCXX4lljLepO\nMKn6VsBmhsfmV8pQfIuCKsOCLItIzw1DCZhIR1S5JxD7V+nogJhMxsw23VIJcj4PUdM4++JHmCoQ\nwhnPqC6wsut5GHv30ME4F9LuTFfFLBWYUJ2laovTh2A5dPUtpdKcurenpmCdHubXywgaCxcffhDD\nf/aJszJXs1NVfOf2PaiW6HE2gaog/afLA/B9A9KWNKb3HkK1bGHlujZsupAew/5nT/HnzRo5A2Lb\nSKxZG4Kq6SnOVBHbxvSdd2Du/nvhWTYMNY+q2garTr9LdesggsgLNGZSq2DNzmHqBz/AyO2388WV\ndZq2AWLpv2LAroIQVMxgobN9BwY6Lsf0VA1jcj/Gk+swQFbiaVyC2akqKiUTUyb9/EQ1BCzmbAGC\nqmL/oIe672IkR0vgy0UTgqrBkkKWZs94+LdnVEES4XgWFfiXCnUwOzpLpc3TmS3Ipve9G1pChqLQ\nc1i/pRNrN1Kdaa493H+UqUoGoNOxPWTzOi64tBcr17bFKoCZ7xSAWHENE8Cv2xTXdEWDasgoUwXP\ni4AqylRJDUxVNDQ9PAYmhZD0JOz7AgQbWL3Ib+yEpZ2CrLZBVsMKtHz/m9C+6mfDHbrB2OctrmBC\nTqWQs6bx336qF3JwTY3CQRTHHkFh9EGUxh+FWRmCWRmCU5+AXWvtp+YHYwKZh6lilX9AqKni7dUa\nNVUBKy8E1zH/5rcge931sCfGUXr8sTATs2xQFaT/nNe2IfBrOc4bUCVq8z+czL9q7p67YZ0eblod\nyfk2uIVC2JFdUWk6IOgpFg2vVoOYTPKb3q8Z8A2DTzpepRJWGpZLvLs8a4yazip4y7UFpFwKvhor\nDJM2nTg7e9JwJicxdfttqDz3DE//qSq95O4i2QNBENC7Iqh+8X0+QbmVMqzTp7m4sN2LMDrBg2lP\njPPzElQFRPFQKx4NNVUBqZTSB5HJJmIrQMeRYRQtEMeHmAC2dh2BLHo4nbsIcwEAzWWrHFTlO5KU\nqYqAKll2oelKbIBXBJ+Dqs52lXvvsOOW29sh53K8Z1oUfDaCqmxex5qNHTh9cg5usYjjia3YSzZi\nAKswUZFwpPta+p2eyatz9C1Ug+LMzkB1arAlaoKoyoEoNpWE5NtwJDroCSC8H5vcTkGVV6vBt2jV\npVssUjF0AOwBChhETeOpEZYCZLo9r1LB+K1fhHFgP+SODvhyqPeY/dGP6DZGlepVgsUDc2Z2zDJ8\nQu/psqtgctaBmEzBnp5C9cBLgCAgsX49qnt2U9H14Cn4pgmnMAe3XEZ513NojOGTs5gcLWP0EAUo\nTTq1IP0nS/T4ScmFYdDfOd+uI5vXsWlrBw68NI6TX70dALgvlb55M63qFQTUjx3jgNQ3TXi1GtxC\nAXbdgicqqMtpmFM0VZYKqtEIAXL1KbiiipnkCjywx8E9o6vC9i6M7QtA1cx0HaLvIWvNoGLQbU7N\nKaipeUiejZH8BajKGQAEAvFx/NAk7XhAKDs2J4QTpFUsQ1m3EVNjY/ipm55Dsotel1KhDjGhwQ7u\nkQ5jBKNOFo987j8w9fwe2hQ+SOk1FqVww1niw5SSgakvvZbJjiy2XtLLfXF2XL0GO2/aEFznCFOl\nhKAqWiyTzSewbnMX3v6LlzZpUBgrFT2e1Rva8cv//Sru3dYqpGQShPic5eWgynFbpv+iEdWOMlAl\nJhIgUxbMrw2jY8276HesSsIvWpDGFyjpD7Sfvrt4UAXENZBOnQK6WvEw/78bWDd49jyeeYypaqHl\n8solPpcAIRiSUmlAFFsI1U2qHw6ea0EQ0PvrH0TPr/8GiOOgHvhGLRdU8fTfK9zQ/vWYP84bUNXK\nXJJFYxPGRpZHbm+HVynzSitBVfnD31jS6tdrkJIpnrP2DCMm7PUq5XASLJV41ZIaMAK14lHUxAMQ\nV+vwqtVYVRMApGsz2HHNamy+qAf2OE3HucUiH2DUoMR3sUwVAFx5PaXELTnJXWa9chnO3By0NWsh\nJhJwZ6fxG79/LX7lt6+EX6vRNJrn8XMTZAXYRDAz+J/Qky58j8D36CSRz04i2ybBcw0kshshH+9D\nva7BKNcAy8eq1eNYd8UcLlw5gLnUCiRUOqnncxX0tD0P16mgrSMJ1/VhR663prhQFAmpCKiyHQJD\nzUOWRfT2Z2DJyVDOTgjkYNUnZbOAJMUsLphB6dVvXI+f/rkL6fXOarBMB3axjGKiG54gwwq63FcD\nwbLqmXyF2PbTVBRsnjwJ1TNhywnYlgs5qFpMX3oZZM+GJdFJQIEHAgE+BCgdHXSlPTqCEx/5EAb/\n6H/CHh2BnMsHwuWAqbIoU8Xad9QOHoRXM/i5RAsonJkZ3noEAKbv/SHcchnO9DTU3j4IQTk/CRhV\nxyoDgguIwF13ncTd39gLubsbdmoUtRUHoG1Zhc5b3gO3XML4F78AazS4B2dmMPCN7+HR7x1AfTyu\nf5qboQNwjdDfKdpP0fd9ruVJKEF6RJdnxloAACAASURBVARqNk1TJgNQffkFaRAIOHFsDr5toz4w\nALm9HUpHJ0RFgdrXx92c5cDd+f+x955xlp1Xme9/531yqDqVq7o6qYO61crJSpaFM8Y4aozT2MZz\nTRh+lxkwMIxnDFzA19hkzJg0IAdgsE0wWDYOyAhkWbJsqRW6Wx2ru3I4dfLO+354997nnAqtlpDm\n5wuzvnRX1Tk77/d93mc961mhbeNV12i2BfjxFZ21U8LpOT/e9eaptM5harCQ20Uz0kv5Un9VTgyq\nLpytUrAWSTs1FhY6/OUnvs0/fukE4zuKXHtVmZZeZGamyZBhMeAs8vQTizz5nTlG3DlK1iKhJJPL\nauQLBm7bojF2EFlqI0mQSkdgdr2DpOk4qgAUu1cfQffanHCG+dbXRNpVip7juJdnrHWKG6RnnXV8\nSWVhts7KYhNFlVFVhZvv3MMNt+9iY8TpPOhnqmRZToBVb7usjfHKNx7mmhftSNJgICb0QuniDtVy\npKmK9amJUD2q/rsYqOoFcLGMIf685MqoRhdEBWfa1D93/7aGqkDCVF1q+i8GJn67jWuvsXL2L7Fb\n4vkKI/bJ6SziWgLI++7WPmzBM2iqlC1AlaSLhf1WQvWtrlmsZ2odfQzJMJN04rOJMPQJIhbPv4im\n6v/ECxvfNaDqYj42vYZqkqaRvaq/g3bMZMViVJH+i1iCDSsFvyWYqriHkN/qTnaSYeLV6wSdmKmq\n49cjOjYaJJ3opZQGDdzlpaQnYBJ2hxtu20UmayTtObzaejLA6FpEYTcam5rkbheVkRw33bGTyxe+\nnjBVfr2OV11DLZVQByu4K8sYpkZa8yEMMSeFViy+rpKuwZDYdzYlfudFjIcshxQKAYHXQlEzpId3\no/kWzdU6gd1Nnw6WxLkOBSL9NDa6jKmeprnycLKStjrd621qYvvxxAsQyg5ts0BxIE1uICucxOWe\nVEU0QEmyjFoo0Dn5NF6kQYpB1cErx9i9X0y6ZkrDsX2WnFSSpvNDmemd3YFO821G3v1eine9lMzh\nK5BUlc7Jp9H8Dq5i4jgBqmehDVYwduxADdxkwtTkyHdLEm7dybMYhklFqlosJkyVu7pCYFl9TNXi\nn/wR1Xu/kKSYe9nT4p139QEEX1JZ+Ys/gyBIyvglTSd0HQKvq1mTd2UZHRH3IxwcI8xGWo2bU6T3\nH2Dwta+n/dSTfQLtEzMW8/m9fOPLx2k/9SRnfvonCKwO1Yg9CSU5uoddUBUDKlkO0PVoglBlrIg1\niAGzZtfJOuvUjAqNh75J5+QJUnsuS7aT2rsv0Zyldu/uVtvWarTaXXZzdV4At9xoNyWl+Q5jU8UE\nUAG09ALG5CR1Y4C53B4kRaHdtFlbblFuz1OI2IcwDLns0Agvf90h9r7oECC8y/JmwFj9aVpNB8/1\n2bH8bUolsRAby7kooUcgKaxqFTQ9Mv5Vui2oXFnHidJ/abfGLWf/nIHWeRbdLCEQZsTzNxAVq+QL\nJpquJJWrmYjR/ptPf4dTx5afsSI4mzeRFQlVlTe5f8ci83xxezF2Nm9y/a07n3UVVcKYRMNALNPw\nmw38Tueimqrec4rTjvFCQ9K0yDYh2nzTI7RtWkcfY/kv/pyFP/z9zRv0IAzCBDg8UyRMVadNY+kb\ntKuP4Vr9Cwq3s4jTjipDm8ubtgFdMLUx/ReGIX6tliwEoZv+kw1DGNHO9xuzBlYnmZt6Qx8ZFTrg\nVgsl89xasfSm/C6mqfo/cWnhOTXCMHjmD26I7xpQlb780LZ/iwcCOZViz29+jNH3/XDf32MmKy6X\nlnU9yVn3rhRC3ye0LZR0uiskbDUTHYoxOdnPVNVrIpWmKMlkarfFyl8eNOicOU3QbiUNL+V0ps9l\n2ZnrMlUxqNKUnsaua5uNFreLw4cHGWmeRhscBAms9hnClItSSaFOFxMxctyjL25t4K4sI+9Iwe0B\nkimuo6FGPc0kjzASeORyPr7XRlYzaJUhDL9Du24RON1B2BwMOLB2P6Wwf/BRlHSyArY7vUxVpMVJ\na+zcO8jgcIabbn6Y6159hspQSK4k7oHVkwLspdIzR66ifeI4M7/486IZaK2Dbih9K+B4sF40J5F6\nRPKXHRlP/q/5NplDhxm6+y2iwWw+jzM3K5gqJYXjhShWC2Nqiszlh9ANJQFVenS/fFlBKw/0af8K\nt98BCBZQNgz82jpn/8tP4Vw4LwTmpsljI3ewlhrBPj+zyVh24id+isqb7k4c1cV+VOoPiPL3uJeg\npGsEjtu3ktZuLrF/r2iKao1fRhBpkULTIwxDctde17ev6pfuTSbGY7M+a48fw11exppfoLrSPwAL\nE9mAVvWJJPWXT3eZ5HaqgK2kUVU5uRfeepW8tUzdHMSaOYe/vt7XXiN1mQBYWqWCUix1NV6+3ydw\nr/tiou3rRRc4ZIoZXLV77Vt6icrdP8BDk9/LU8PCFDM2sixb84zXj/ODP34L3/+2q7nzVfvRDZVc\nwUy0ScWMRKV2mrf/0A287d1Xku2sMDwtgFxl/RSy7+LLKovrIYND4lhU1UsATdNTcRQTNXDRpgwk\nXWKwdYGOlqOtFQij6r/BIfFvOqOTzuiJviobRtqXyLU/8C8+eMuyRKGUQtU3++akLoGpeq4hZzIg\nS8mzIxkGxtQOql/9MkGzeclMVXcMj9LqqoqsdO9x2PBQcnma33oI6/Qp2sefYlMEIdgBgdcR5q89\nelmrcZqVM5/p+91W6T8AOXqOZDWNay3he+LZ9tpbZw/itN/G9J/fbBB6Xp+/UwyIJMPA3LOXzumT\nfe19gk5nS+sgSZYxd+8Vx116bu7uScpPUi45/ee7LRaf/pNtRfrPNezmDJ793KxdvhvC99rMPflb\ntNe3eA6fIb4rQNW1f/BxRt71gxf9zK6P/gY7P/QRId7d4DAbM1Wx/knSetJ/PUxVb6+qeAXWOXGC\nlc/8OUo2hzk1hd8QKzCI2KBGXVR3SBJh4OO0xT6UPRmcw+eRyt0u9FqlQuh5nPyPP0T7xHHRLgPB\nVCXpv54x8VJTgHMf+y0W7/ljcW7ZDPprxrCHZ9BeUqE9+BTBkRZh0aG5+u1ETxUDPXdlGWVfDqL3\n2MhMgTMjflB8/Ia4limzDaGPomZQS2V0v0PHCfGDLoskqTKTgyvIqf7rHwQ2qYyOqslYHRvb1vB9\nmWy6zern/xp3eZmXv/4Qlx3MoighihIyOnQKU3mKQr6BcqRbPt276hv+gbdx6vb/i0fVg1z4xw9T\nMf96k99MDKpqqQoZp0YqMr0rV7LssE6jeR20XKaPTo9ZR9NQ8GUNy5NROg2MqR3oI6Pkd08nLER8\nv3J33EX6wMEEXMumSfkNd3PhujehXnkdkmZwunSETpRCk3SDWkdhOTvNcmYK+8KFPudkW0khlQaR\nJAmf7kPhDu1g3aiwbg4RFAUrKUdMldej+ZByGpouBut2cQIpZgWkkDCw0SqVhOlClgk6HdxsPFhL\nLC0I8L0+u8LG+dzquFiN06ye/QxWIypJl7qDrm1msdU0qZSCJEm4qyt4a6sU7BU8xaA6LwZTJZfj\n2w/OsLrUJLU3auo9MblpUmk1nchgEnbcsM7l+0/2lfiroUsqreHJWlJl29RLGOMTfdtZXmiiajKH\nfurHGLr7Laj6ZvYn7qRQzIm/ye4saxf+B6QUxqcKfM/QBcwTDyE5FpaeZ23N6gFVPoMjAiQ1LAlH\nSZFVauivHkHZn2OwLa5VtbQrMXQdiEFVVieV1pJKytjOoHvNn9kCozSQ3pLRuhSm6rmGkk4Ls80Y\nrAQBg9//eryIAb+YUD0GUoraHS9ilkbSNGSl+y6Pv+8/Y+yYxllaIui08dbXN3nUEQRgh/h+h+qF\ne5l9/KPJnzr1k7TXn0hSg2EY4Ms2SBJ+q4Xb7GYUMiVhapopX5H8LvQCgkCM+xslHdtZKsRZit42\nMpJhgiwj6wapvXsJbZvFe/440RgGlrUtEB19z3uZ+E8/yVjUI/LZRpzy08zKJaf/2utPYjfPUpu/\n7xk/G4YhtYX78eyLkwFhGLJ8+k+pLTzzNr9bw3cbEPr4zrMHht8VoMoYHEhKx7cLNZ/ftkJQLZZE\nW5o4/adricailzmKNVdKpguq1u/7Gn6rxeT7fxq1PEDoOH2pCb/RSDxInM4ChD6aWUm2KQ8ZFO98\nCaPv+xFy1wlwELTb2DPnEpDn12rJYKjJ3YHiUsXqscUDgJ+qIk9Eq70ed2H5ljTV81/Aa4hj10dG\nkVRVtFExZHAlnM/N4Z2oEoZNVNVDUn3sVuRErUSaCTWNVi6hex0c2cQLouvYMgn9EPOaXSgDuV5f\nT3ynRfuJo+TzOq1GG89XqDcyZLMNVv/ys1z48C8LqvzEg2JbIZjaBfzmfVx/zVHCsa6GJBaUx7FS\nD1nI7ETZkSad61Aa6H9OYlDV0fIYXousXUWWoFAyOaie57azf7YpXRyzYblBsS9bMlADJ2FWdENN\nxOmR9yaF73kFF2abSbpDKRRZXGxzvJrmzHmLFTfFmYGrOBH568iGztJ65Puk5XhM28/8kqhCbOpF\n7t/5Zr75SOQr1vMaPq4d4FsTr+RbE6/kvq+cFfdZ1wkdF7fWn7rQVNGYdnaphpzvDtSLs2Kwz1x1\nNbJpYk5PA+CYeYYyHlIYsLQqJveV05tNO622m6wy7ZZ4ntJyd+XrGikBqtQAZ2mJMz/zfta/8mVK\nqljArETn3QjTfONrp/nq54+hlQfI33wL+Rtv6uudCNCyIeW3UH2LbNGiWKz3FTeMv+MdmJF1QOzY\n2zRKSD3jge8H2JaLmdIwJyYp3fXSTecFsP/wCJO7ypSLYntOc54wtJFLGlqxzMjVlxPaFtSrtFXx\n3ucL4t1VFZ+JHSXMlMrcsoOjpsiZYhEjZVRMr43iO9i5wcTINV80SWU0YScQMUr79pxh6g1HkCRQ\nI41lb3XfdnHTi3dz1/ce2PT7fCmFmdISz6rnM+R0BiSxEB374f9I6aUvJ33oMENvewepy/YlTYG3\ni9f+wJW85b3dRVOiqdI04T8Vv2eFUZR0mqDTEfIL3xea1dWVbruqMCC0QwKvTXPlIQKvRRBVOsfi\ndT/qmVi98EUev/+XkHMpavd/nc7CKYzUNIPTb6A49pLo37vIj9yG7GQJTrcJsLBmznHmp36C5mOP\nJsccWypsTP/FoEof6gFVkkTh1tvIHBIeZAD1+7/O0qc/KY6v1drW5FrJZkkfOPice/DFKT/NHCLw\nO332NttF7AHmdBZZOvWpiwImz6lSm/8qK2c/c/HjcGsEvrWtRu3/DxHfc/8SiyJ647sCVP1LQ1JV\n0bw2as4rb6OpCtqRB0gqLVYUikLoOKilEvroWDLZxv3cYl8sNZroYw8QM9c1SVMGSqjFErlrro2M\n8kTYF84Tui7aYIWg00HF59pbppka7KbT3LUeA7xtIgyCSNslBgtbnYU6eI/VwJCR5YhuViTC0MO2\nBAul5PKJQ66UUZFaOsGcRefoSQDyZh1FC2lbGYJAgkAci6JlkM0UpuRiqymcIOqpphUIF20ohWjj\nQ+jGON5jNUIvoP30k8z+2kdRzh0HfIJApl7Pks6La+9V10TVy6qwgJidG0IKu5R8J4z6qqVSfRNu\nEAS0m/0D2Xh5kc7p09T+SYhae0vEDa/N5PqTXHN5BlmWcc6La5G7/ob+axrR8cWJ7mCoBqL8H/rT\nFkakgTvz9Ap/++dHeXxOJkBCLRSSJtFL83VaEaOnRAOZpGosLIrzr5lDzOcvY8HLo01M8vjw7QCJ\n874X9utcRqzzDEnVpFm1pGkEjo29doGNoaoe52YXkFIqiiKe0/u+8ChWx2XgNd/Hjv/+82hR/zsL\nnWIlR9ZeoxqIZ3XlKVFtlM9FmpfAwfdDnE4EqpYF6DLl7nvk6waWlkO3GzQf/ib4PkGnQyGvIocB\ndVsMKzPRmiG2Exl513vIXXNdMqlYapqHJl7FYnYnRuiSdhtoqodpOH33NTs1kaS44mgbJdo9fRs9\n18e2vGfUJg2N5nn1m65Aj4wpfTta0Wc19PEx0gcvRzJMZK+re9O0MLnW2bzB1K4BLsy2sRWTjBkV\nx6QVzF27Mfw2jpFLDIBVTeEN77iGq2/akXhKTU3NE1jHyeYMhkbzvONHb+b1b7/6oscNIr23VXPX\nq26c4o3//poXxHVaNk2QJGRdJ3vV1ciGgSRJFG9/MZM/+dOknsEwcnSy2JfKjcfluPpNVgwkWUNW\nTKEp6rQTHzhvvcriH/8Rs7/+UZHu8zxwQuzmuWR7fpS28iOGKmZzmyuiKEIuZfBr60g5FdkxSJcO\nIsmK+FeSKY7egXF+gmDFBjmgdUxYfrQffyzZR69QvTe96CwtgiShDnYX2QDDb3sn2auuRisPoI+N\ngaJgz5yj8ci3cGYvXLLJpkhxXrqmJ2an9PRI9PMzg5pYh+V25rHqJ2msPExj+aGtPbl62K8LRz9C\nc/Xbmz4jtiXApn+JBQWXGhtTvi9kBF7sTfZvFFRBf4Vgf/qve1FipkpOp5EkKQFB2oDQUiQ2/bG7\nbbOBX6slRqNhtCrKlK/AsERFnjbefaEkswsInHkxGcXsh1+rcd0t0+S1Ls3fm/7zGnXs8zPY52fw\n7a4zud9sCto7OqaANkqYJ2z7SLpMGInN/dMtFDWLI0XpyWwWtSzSPVJGQfKjdid1cQ4DWbHvhpPG\n8w1cK3L3jvRNmZRCKCl0AjH5mAMTUJPxpQaes46WG2LHG34OGgFuTXw3HbaR5ZAgkKjVsyhaiBSt\n8v3aOnpGAI65+e41C0I1MWns9XsB4Y8UhpDKaHQ6YkLKKWdY/vQnWPyj32fmF38eQ+0OOobfoWQt\ncuiw2P7w296BPjJK6rJ9fduNNXTlPd30UWqwmPSa7GspFOX/4p6Aj51y+Nqed1BPDyfNopfmGzQ9\ncX31aGXjVqssRG19vCjN4Sgm0v6raBklpDCg03EJghAvkFB72h9NXXeA6St30m451Nc7YGZw5mZx\nVi4Q2v2rT11zkW1NtNEIxYSrKi5PP7GIrOlogxX0oWECScF2ITcyQMFeoW4OEiBhBQpSGFAeFt9N\nO+KYrRUB4Jy16N5mxT1yXRVJCbHUDMrqHI2HHkyORSuVSMsO7aityNnZSOC7YRyMjTofG3kJLb1A\n2qkxJFXJOGtomodhOGh6d2jSDaUPVOVzGraaZn21O2g7to9te32dCy4WSfWvHaXLd42DHoGHI1cm\n4BhE2g8glYbhsQI79gwQBBa3vvwJRnZHDXUHcpgv3UPBqGEraTzXR5YlFEUmmxcidTOtIUkBuuYR\n+k1uf8U+br5zN+mMjmFqPNfQNKUPuDyfIetR+5/ic9P5bNperKlKQJWJoheQJKlrVxIthN3FRTon\njuNVq7hLi1inTyMr/dkKz1kn8Kwk7edtSNco+RRkVbHobG0NUPxmk7AZGd/OikVnb1uqLkMVJL1N\nQVTxqqVyn2fixpj6rx9k5y9+CICFP/g9JMOgeMed21+gnlif+3suPPrLCWvyTOE7NWQ1jZ6KDIGt\n7qLdtVaozd/XZ3kDXaIgjsbSA1QvfIFWNfKTC0PW5/8Bz6klAFaKNFtrM38DQH3pwUTsD4L1gou7\nuj+XaCx9g/mnfvsFA1a9IDAGU5dq39Eb/2pAVewsjaIIJ+qtNFVx+i9KG8QpQDXSZPXSrrGTsLuy\nnKT/4gdSVkzylRsJLR91qOc7PY1F41SkMTEJgHX2jOh9FlX8KYVCkv4LPY9z/+1nOffBD3Dugx9g\n7pHfYn3uK4AAI0koEiE2xuAOsCKTyNDDsHbgfmER3ZzC15pIhoHdOQfXS6gvKiOZCpIrwEFYj/yF\nCmIyqdtpQimdeLHIMaiK0i91K+pBVhpn+FXvAALCwEZRMyiZDLKWRjJkzEO7qRzagyIH4EOtLq6Z\nVBGDcufECbRMgO1oWNUUUlTxZ+gWaxFg6RWpQ7fa7/pbdxIVpiGFa7jVNdEkd26W6if+MPl8KmIU\nYrBcuOU2pn/hlzZp8Ibf+nYyh6+gvG83UpTHzO3oAqzeiTmfFd9dWVzj8METaFrkF3XoJhbn6iiq\nTH3dYqkRVXVGdHqn2qDTdvtK2MOxaVq7BCOx21jFcwPq6x38ALSeFVHlwC5GD0wD8MnffZAHszcJ\nr6nWKpIlJ9cOQEvbGNHprVfF78uDCseOdge53O13YrzzxwDIFtNUciGBrNFIVXCUNIbkJem24piY\nPF0rYgDcFrIUoqcEOHIdVdxjQG3XsM+fR4ucmNViiazm0dbyuLJOrRb1Emz1V7nKpokr6zTMAXY2\nnuKmmc9xmbFMzq8iyyGSBEoPM6bpSl9qqzJeJAxJ+taBME3VlWXGR5/mUiLWxrlVARrlaZPZox/B\ntVbJXX8Dcs/kqURVf+NTGQaHs0ztKjM42ELTfDID4nPqeA5LeZodV0s4WhrPDZLUXhyplIaui+fH\n9xpM7ixf1CPq+YowDDaBjd7wvQ5rM58n8DdXI6f27kVSJNL7Lp7mu9RINFVqxIyqWTRDjL9KKiX8\nxyImuf7gA0lBQ/2Bf8JZmCdjXE5p4pWUp14DQPXCvcw99TsJqPKdWh9wkLIGcj4y8V3bGpz4rWYy\nLrqFZbS7hnDWF5O+fr0C9d4UoLu8nLSJ2vZ8NQ1tYJD8Lbeh5nMMvOa1SeX5M0Vr9VHC0GPt/Bcu\n6fOes46qFVBNQRJ49mr0b5X5p36H2sJ9dOr970fQUzGo9lTXxgSCay1RX/g6jeUHEzF7nDIECAKX\n9dkvsj7/teR3Xaaq9S8GQJ3a09QW7gdEMYJnr12ypcazCc9ZZ/boR5Km2V0X/UsDtL3xrwZUGTuj\n1jaxg3iURopBVevxx1j4g4+Lv8Vlr5mYqYpAVU8VRyz0BjYxVZKsYu7eg5ouJVoWp7OUuDoDSSrS\nmBSgauH3fpfafV8jtCPH6OGRpGKv9cTj+PU6A699HdrwMKFuJ94pvR5aUiSuNUd3EVrdVZeiRSCR\nNKg+ajlPY+UhwoKDeqUAff6qOJ78jbeBB9mimMQtR0fVstF28iiaONfBcgSGbDGZqXohaSEBXfCl\nZkpIAzrcFjJ8JI8sByiuzeXnv0nY9lEuE9tuHn0UNSPR6RhksBnZ9x6K4y9DksBqrxOms5uYqrjP\n3/BYHj0qaw+x8dfXKb/8FVTeeDf2U48nDvLpqLpxuxYPdnOGtZnPY+7Zy/iP/TiKrpHNifMrXH4w\n+VycrpIDl8nhCPzJ55iaXOD66wQTuFwPcR0/cRNfbcRVghr5W24l/UphbDg62QXdnpljYa6BFthM\nj4pJZWWxieuFaD0Dd6FkJqX4AKvrLjs++AtoUxVyk5cl1UsAE5PzHNwnUnirq+L5GBrWWF1qJu1W\nvvWtJe79R/GsZbIGEzsFcGqMHcRWU6QMKUlNjVwu7nEQpWclU8EwNRRTxvNUQh9KigDk5ckhBt/4\nZkbe+R5AMFU5U2jI2pHBa6GUotW0ue/e43zunkc4c2KZpq8mLV+mbjoCiEVIvkcMHwatxNtJ09U+\npioWf8+d6+o/XMdn3+6HGSodw7uEtEe8oPKakSGrUgdCXGuJzJErKV4vqicNUxXdxSEBHbqhcuSa\nfmbIixi+TMGh1XJxXX9Th/tUWsPQYw1QaxNr8EJFbeE+5p74jW2Bld08S3P1EZzOZn1dMikqm6sO\nn0tIsoxkGAlTNTD9OsqTrwLYZM/Q+s63hd9bLk/1XgEssgeuJVe5lkxJFAeJSbaZnJvn1nGtrtBc\nyukoVxaFpnO+0dVn9UTQbBIu2gQnXeSdBsq+LMr+HJ2TJ7BnLxD4NlLkJdebFnOXFvv0VBeLkXe+\ni52/9GHKL3vFJX0exHgM0KmdoFM7wfrc1y76ec+poRhFFDWLJOsJU9Wpn0w+06md6PuO7zXRzAqp\n4gEGd72ZVEGYI3fTgsvJ92KmKtaxAfiR9tJqnEpYHSexrdje/qKx9CCNlW9d9HwC32H59KepzX+V\nMAyFppkuWHw+Q1yr7j4SpurfcvpvY79ASVWRVDUxk1v69CcJI1fqhKmKVgxaWSB72UwJrRWQ6bF4\n6IKqyJ1c1pAkCTVVJPDbuNYqC8d+Fy/c4FkFGFPTyf/bx48nTJU+MpKk/xoPPoCczVJ++SvRJkZB\n7ooO49USdEGVahQpvfjlye/lCBRJUTm6sW8Kpz2PrHZXRN6seBBLL3kpWqZCuhitzFwNMy1eXjPX\n9bGpjBZQApeVtSItew96ehxJVtEzgtGRlcifqDSUCObTWQtZDkgNDDBx01X4T9aRd6Qhp9J5+gRy\nVsbuaOTtFTRzED0lBqR0ykK6/Xsp3Hpb37WLHdSzeQM58osi0jhpQ8OUXvoydn74o5gZcd6ZlCoq\nb7aprqkt3k9z9ZHE+A+gMCCukZntTpJBVOY+Vj+JnjYwUxquFwMWF8NUWYxSf5ddPtxXdeXJGiPv\nfHdSaTc62QWK7ZbD3Pka47uHmP6+lyPLEiuLDTwvQKULkrN5E8PUyOWj+2mqqKUyoWyTLo2h9ICq\nHUMrTIyL5259XSMMwTBESyGrLc4zNpwEyOR0hu64lYLmsJYZx1FSpHNm0mNPWACEyBEjpxUzGGkd\nWZdwXQX8kJTu8Zb/cANX/8hbKb/sFZi7dlG88y6yV19DLqsQyCq1wlRy/p4b8OR35lmYrfPA107z\n+fuqPD5yB4Qh0y99EQOvfR1Db3kbWbrPuu820DQFVRO+TLqhJnYGsU1BnH4FAao8V9wju3F2y/sf\nhn4y4cYLq41l8p5TQ5IkzAFx/1JpLVlM9X42m9kauOlqjcAPaTXsTUyVmdbR9e6z979LyBt768Vp\nmY0RT4bhFkxVXJEi8fzptWSz2ylA1fMo0fglb1GIlDl8BelDhwg9T6SyxwQYl2Slb3yLj9N3asnE\nCMC4hzKdxvv6Kq1vPMK5//5fE+udOOImxs4Xz2N/+jyKVETZmabx0Dc5999+lsDtJKnDwO+wcvZz\ntBeP4zcaiV7xhYg4NRcGNvWlEBX0wgAAIABJREFUB2gsPbAt8xOGIX7EVEmShGYO0q4+zvLpP8ez\n10CSSZcO0amd6NNp+V4L1RigsvON6KkhKrvehKLlE0uGWBbi2Wt0GmLx1stuuU4kYQkD2rVj4jjs\n9WRxHmzRLifwXaqzX6R6/m8vev7NlYf7rkVyTPalG2fH16Y3PblVxO9izMYlmqp/y+m/mBHqDdlM\nJUxVDJyg2ydsY/oPQC0VN/1OyQnQEYZdpgpEpZzvtfCjMv7Q2PDAKwpquczOD/8q6csP4S7OC+NH\nSRINnSP9QOuxR8leeTWSqorKOrqiQH8rUKUXyfXYEChGpA+JjBPV3QMEXotc5brkJUxNRSXtw8Oo\nZlcfMZaTUZSoiW22y84ZgwPk7FVcV8PTbktAVH7opugYBKun6N2BTZZaDA6nyZREpab3hLgu+o2j\nBM0mUk6luHieyZVH+7aRTlksFXah7u7XPjXrYmLSdDkquZeQDBnkbhmzmsuTyovBOJPVE73cxvDd\nFlY9GhR6KN1cQQCiXoHz7gND7BkK2L36LWTDIFcwk5SXa6+QzuiJt1KxnOZ1b7+aA7szpNx6op9q\nNcQENTKexzBVzJSK1XZp1CyGp8poaZORiQKnji3jOj6K1H124l5t194yTSqtEQQhvtckDD30VBlZ\nSQGbdRyOoyHJBqoqntNYyK32tDVJZw300TGmr9rFakejo+XIVQqUBtJIknAANzSX2OQ91MS1SadD\nJNvHtFsiHWQ240I8JEVh6C1vRR8ZpVCI2sWkx6Pz74LKvZcPUat2cN3Y9EjCMDUGXv0acldfg6r1\nDPZuA13vepJJkpRUz5UrMSAKKQ2Ke+/YHpYVOcI3zmy6NgCttaPMH/sfBF7kai3LoPbrbOLVeAyI\nNF1NVua9oMNtLyBF5pVSj8+YLLW4+sonCb3ZvusOEVNldFf5dns2GbxfyFCifnqetfUKP0l1BNuD\nKp5HEbxWqfRlBeLYajFUvONOht/6Dqb+yweY/Omf7Xu3VX2jaF/Cc2p06iejdwTClCsMQ090J0dn\naQOoanYXHeGaS7q4H2nYoHXyUbRXjYAU4C9HvSabs7SrR1l7WOiJMoePbHmOnlPbMp16qRGGAYHX\nTirN7dZ5wtDrY06CwE2q9YQxsJe41KvGAIHfoVM7Rqd2AlUrkMrvJfA7SaseEKAnBrVxKGomWdS7\n1nKSlYjtBXqNRWPALqtp2tUnCH2bMPTQUgJsOp2FTc94ffX4JV2D2P0ewGl2GUZvA6iy23OsnP3L\nbasdO/UTLBz/+EWBVTyHJ2yc/10oVF9dXeX222/n1KlTL9Qu+kLWNpcTi6bKUaltq4lsmgy85rVd\nI7pIrKr1gqpIrC6bqcS9vD/9JyNFZcCKmiHw2gklLGc0dn3k1ym8WAgRlWxOrBpKJYzJKey5OYJO\nB0nXE2G9NXOOwLIwJgQDpBTjbuotwjDAks4h78mgXJFHvakMyChari8FpKTExNV+NLJdyIuH3sxN\nJ6LFode9jd2//ttIipI0LQ2dgKt2ZzAyApCaPaCqfN01VIbEPrK5LhOTLh5g/NCPY+amxTn3GPh5\nTg1ZCpBkVQyQTZ/wnIu8W4OsgqTJyB2H/JErxXHreUCiUPQ4+vAsD/7DEzzywClOPiVWR82GTTZn\nJOkXRYleflPpo93NlIaiSAze9iIGXv19bBWi11dstNhlHGKWqRdUpTM6NxwwUEMPSY9AVaSrCX2b\ndDYC1bIU+Q/p3HjDEBmnhh+BqrhqMZsz+XfvvZ6rb+5e29h9/sCRUerrFmvLrT5WI/AsAt9hYmyO\na6+3cB0fpyMGEiNdJlW6Gkfqgurke4GGoqVRVTGYnzmxzD9/9STtloOZUjly/WSSRhubKhKEIl2Z\nLecY31Hi7T9yM9m8SSnXw94EFoapIulgtBtgOYQpn/ljH6N6oav1CMOA1ZnPkx8U16mqlMnljT4W\n78bbd2GmNHZMi8lw2OlPN0lm9xr4bgPNUNF7jN1SaQ3dUCLmUrzDMWhrNmy0KLXWqZ/ckpXx3TqE\nAb7fSYTRiSAtijiNFKfuNE3exFQFnoXnVMkMHAFJ6VuMAIwOrzA+fBxV3cBUpTSMHqZq9exnWTp5\nz6bj3CoC33lOq2boskxOp59JD8MQz20kE8dWDYO7VQbP31Qx/mM/TuWNd2/6/VaWOal9+5ENA3Pn\nrk2aS0Xv/1lPjxJ4Laza06RLlwsfLAlo+6j5bho+7hcJEcPT0yBdMgzSlUMiE/GiEsp0dEwd8Vzb\n1bNiG80l9LFxjIg52xiLJ/6I9fmvbn8RniGEbihMMgOxj1svu7ly+k+Ze/I3xX2Mntv4mkg9ra88\np4qiF9CizEDM9MRtbRS1Xy4ha5mEYXI7S5jZHaSLXXlEL9NvNc4gyRrZgauwGmcSllCPQNXquc+x\ndOoTfdtfW+ixq9hiUREELr7XSYAOgNWKqtrVLO6G9F99/uu0q4/RWd8arMUgsrdhtrehz6PvbGCq\n4ncicAmDZ7am6I0XBFS5rssHPvABzG38OF6oGH3v+6j8ux9IfpZMM2GqvFqN7HXXM/Ca1yZ/18rl\nPoADJBVgSipN/mbRkFftSf/1ivRkNS1AVSyS9NqohULUTLMLxgABmnwf+/wMsq6jRZV5nRPiQYgr\nEKV8DA5DPKuGV6yiHs6j3TqIZCpAgCTJKEp3Vaea4pitx48ROgGuMw9IaKkRzNxOZDWDYmQSZi5d\nPkw44+P85RxqOkt++GbGDv3fqEZ39aiXSuz+HsFKFTd46PSubOSe44hFopKkdAWpZ1VQQD0gJtKB\nV72W4be/U/xNklG0PHsPpBmbLDBS/BJl/VM89I/HCcOQZsMimzeTtKtqRuxYQTjid+onaa4+Gjll\np1Em06hHtvZ4aVcfJzb16mWqdu2rcPDKUbL5fu+k2NVYzecjUNVlNIol8f1MzkgAuqSqwo5AEYNZ\nqymAjKLKpNK6AIdRxH3Qdu0bxExpqJrM4e99ETffPs3Lvv9ylk9/muqFv6Ox8jAZ/Zi4t63I5iPM\n8snfX+Tv71Vw3f5Kt1yxiKKmkSULWfZ59KELPPrNC6yvtpncVebmO3cnx9vLIKWz4jxiE8nSsDgH\n29ZEU2xTBSUktAPwgoTdaa48nAw4TmuW1uojqLk1zGjgL5TT0TUKyBUMsnmTN7/7Wl7yyr3ceuZP\nuTLsHwSlHoDjOyL912tvkUqLKjlJkhJh/fCYeK6adRtDdwgoEYYeiyf+sK9tB/SmuQR4UDIZkbru\nCW8DU6VqSjKJhL7Tp+1I5fcwdvBHyQ3dKLandZkT19XQNKkvXWOaEobhEPZYaGzUMbWqT2zp4lyd\n/RJLpz6V/Pxsyu3jRcTGFi128yxzj/8adktMNlsxVWGc/nsemSolldrSl7BXUzX5/p9h96/91qYi\nk97QjAEkxUwKN/LDL0JWM4ShR7q4H1WL2KqWR+XNd5O/WTjv97ZECzod8H2k6Hi0yhB6ehTJ01Cm\nu2AjqAlgHd8vqaAmvoQbIww8fLfex65carTXj1NfejDJVBjp8b6/9zZ9jhnZMHC6DGu0YM5XbiBd\n6pqbqnohyQzE7Fb8fshaP6iKmaogcPGcKppZIT/8oi2P12nPoeol0qVDQEgjsrKIbR3iz/RGqzZD\nPBZvpfOrXvgiSyf/RACfqELJ7QjGTEuPbGKqlIida1Uf27Qt6FZBxgstq3GGuSd+re898yIA57ni\neHrniI1sVRC4FxXgvyCg6kMf+hB33303Q5co4nu+Inf9DZRe8j3Jz7JpEtq28Hpq1DetdAp3vJgd\nH/yFvhc8pqXllEn5e7+PHT/3/6CPitWIAFXdFYASPYyeE1GwviWa3g4vYb5vF8qO7iARVwF2Tp9C\n0vTE7iA29YxZMSndnURmf/tXCFUPabA7Ice5aklWk8FEzXSBhOSLW2pkp5FljcLoixnZ9+6+8zbS\nY8jHTcJlR3h2STKqtrkKaXrvIG9+z3WUBrcWfgPIahdUeW6NMPSRJDWh8mUp+m5WTF5qOt/vbq7n\nkMI2o1MFTMNClkMmR55i/nyNVt0mkzMSpiBehWnjFSRJor74ALWFf+CmF+/iVW+6gqWT9/SxJ8lx\nOevYrfOkCiIFGva8MMVymttfvg95w+Cd2refHT/3i8JhvWgmZfUAuYzF9NQs+UJPY1tNRw1cvIi5\nazXtvp6HsRBckoRZIwg25A3vvIa3vu9Gdh+e4MhN0+y8bAC7PYdnV0WaShICaidqofH1Ly8k5IGz\nAVSVh8siJW3NcNcdD+I6Aozalkc6s7UTPdBntAmQK4odNJqZyOJAJZR8sAPw+weTTk0AI6spBvhA\nbnFo8DFKxRqO45FKa7z41oe4bO9CtC8DPZNG9y20VP8xEQEcVS/huw32Hhxiz8HuGLLnwBD7rxCD\ndQxSh8cFkGk32miaD/peRi7794SBmwzwcSTi0wg8KOmMYKqiVkyKlk8mJ0WVkaSQysDpngE2pL54\nf3Kuemok0gSJd8fMTZMqHBTmtqbNwT1fobEcG96GLJ/+A3ZNz+L5Pc2RzeG+Y1w9+xlWzvwvNoZn\nr/QJdOsLX2fh+Bb98baI+Phda6UvRSImtBAnar0VbpX+SyaP598Da2P0pv/UYukZq+Tyw7cwuu+9\nCZBQjTIDU68hVdiHkZ3uFvBoOdKHrmDkXe9BH5/oa2ge66liOYFWEWOLrooxP5ixsD91Hv/ROqEf\n4odi8lWGcxS3EZ3HgMixlp41y1G98Heimi6q/lbNQaQe5/mYqeqd1APfSuYgNR4jUxUGp1+LGldW\n6gVkRUdWMwkoiTVKitp/neUIVHmR/lBLCaA5euCHSBX6JRogNL6aOYSi5RMhvGZ239tes+wgcHE6\nVcz8HgBaa48lFXdxOO053M4SQSSiF+ddQ1ZSaMYAnr3Wd/5xWr5Te3rLlGv83rix0D0Ccu3qE8ln\nkuvq2wS+FTFo8SK8J3Xcnmf26EcTH7St4tm3wn6G+OxnP0u5XObWW2/l4x//+CV/r1J5/kuLl3IZ\nvGaLogEEAcWx4c37GRvo+9GdHKUKVCaHMQbzMNRdfTYXwFH1ZBuKN0D1AsiIG6IpDvmsw3kzbl+Q\nSj4bFPdyDsD3hZ5mzyRnZBnrlKjMGN63gzNP3kPb7+pBPHcdXe0OusF32hz6kfdjpMU2F/QMnisx\nMjVC/FjKikqAx+7D308qYcr6zxFgdaBIBxgYGyS7zbUfGsozNLRRt9AfhlRi9awAeaFvE4Q+qbRJ\nOTfALJAdHKDJHFJKTJj5Yo6Bnv3VZ0tYrUV278nTilLelcEqMyfXaLUchkZyFIs6c0C+OEi7ChNv\nfDWVSo7F400Ct8n4RJEwDFiLilzKJR1FFQOR6zQ5e/ReAMZ338rJR46TyUh9x7BtROc+MVVi6Ux3\ncCzmlxk4cIqV+mj3earkGLnlRmaPrvOnv/dNqqttdu+rJH+XIlKhNJBhZKQL7jc+j1Z7BUIfCZsw\nsJBw0XUXKWyiGQWWF7rlxBuZqr37RpGsaJLQPDLpDq22mFiGhnOb9pXNGTQbNmPjxb6/FQsBYQDN\nZppioU6lvAayR+gEoPZPrnKwQKVyA9VzQv/gegsM3+gzzKMYxUmGKjKLKZtMoZPs48TDH0e7aZCw\no6FkfMppMSm2v/+VrCw/SCo7QOA73PmK/jL+yl3dYxyoZBksPEZQOwPsTFbd+UKZscldtFcO0lr9\nFjsPvIR2fZb8wF5q5wU4/51v/y4/++r/F2Mgjy3X0ToZ1HKW3MBlLJ37OuWSTiZtUC6tM1I+itNT\nwV2b/xpIMpqRZ2RMtAHyHJmFY1AojzK66y6+8KnfYLB8AVkOUcJVKpUcjrXO+aiiV9NTlIf3szb/\nCLLsJ9fFtZvMbPNcLB7vEPgWAwMpZFmlMbeOb69e0ri5ejqqMgx98hkHMyMmPD8SX8dgyjTCTdvz\n3Q4XgGzWfEHG6L59ZVXi0a8yUUHLX8r+BmgtDVCzlqgMVdDNPbBH2Jasz4hnf/DIdQxHViEr46Os\nffMhzr7/PzHy8pdSPCLYnNzUOKuzFyhMjVOp5JD33czZ4+fID+5jpTpH8eqraC8vIo2IRVOIS3lA\n5diDv8bkvtdQGulqq5rrEfgNfbKpJul8fzuli8VKqkjbbWBFovDK0BCNhQE6DcH2GJpNpZLDbq8S\n82DFvIq1toZmFBge6TcireWHqS2vUhoYZrCSYzVbQQrrVCo51ubFgFkZHiPdc62DVpnGko8mCVA1\nPDaNmckBOZxajk4k8x2auoWlmftJZ8Q8UTs/muilhkfHmI+IIEVVkmen3ZgDQipjl3O+/jSN5W/g\ntk8zufsnxCULAy7YohIPIFsYpdpZxHfrpHNjFMrDNJZdsI6yMvct9t/wI9Rn4yrakGJBwkh1zyUM\nQy4cFSDStZYYHMyAbbAGhP56clxzTzRR1BS+1yGXcVkILXSziGNVyWclsqUcdqfKsSf+lDCwkYPt\nKxCfd1D1mc98BkmSeOCBB3jqqad4//vfz8c+9jEqlcpFv7e8/PxXwniyitNssXRarMQ6ivGM+5EP\nX83Iu2XqoQ4bPmt1OgShkmzD6ogJplUXtGKn1WB1uVvmHWhK3/6UXF40bJZVVqsdjIlJ7JlzyJkM\nSyuzNKriRQqDEEmWMA5OEtJF3uF8SL1lQCvapmQiKQEra21R8uz7GNXdZG+6iqaVpWltf65eRI3X\nrJDOFtekUsld0j2xowlHT09iN88QBh62HVKLhd2aAWgJqGo2fYKe7XphCtuqkS+L1fR6vUgxv853\nnjwLoYwsu6wsi8nIdsWA1g5DlpbqOJ0qYeizOL+Ea3cp/cX5ObTIq2V99svUV5+mOP5SOo4ASbXq\nOoF66c9brmQytSuPJKmEoYcUihdK1d3++1sqAVWmRh/BdyZQ9ZHus2KLCT1XMDdd1/W5r6GlhsiU\nLqddOwuAY9WTVVcm3cFqrWKmCtTXO0zuLHH+TBXX1QjCkIX1LGOlFqmcRqjtp9MUKYpioYFhOEyM\nLRKE+1iYW0jYVYBrXrSD++49gR8GzF+YoTr79wxMvQZZamE7Op5voKoBKl8i8EHND+DVu4OJagyw\nvnoWY7FKoyq0FWFPubWmLbE4J9yvFbnG8nKDwLdprD2NcnWeY1+a4esPf5a3HnijuEZhgKSY+IGO\nY633XacgcCEMkn5xh64dp3l+hnYdVHU3nZYY6d1AZ3m5gaSN4rlPcvqJe2ksPUBp8pV0OmJ7OhKP\nnn2aTGS4KTk5Knt/kNba4wDMXzjN2poooNgywgDFGO47vp2H34ITjrK83EDR88hy1KOwvsbycoP2\nek8pe7BKduSHcVyJ9vpT0XVxkokUYGmp3pdycyzBkCzOL6DqBTrtOkHgsrQkVt2h7/Sxxr3h2G0U\nLYfvNlicnyFdEJ9r9BTBALSazU3PZpwybbYc5BdgjO6NMAxF8UAQUG15SPal7c9HMC3rtQCp0fM+\nRkyV43XfuSDSVjnr68x88tMsPfBN8eFhkWbzskWWlxuEqd2UJl+FoUyyqv8TmVtfTOuRT8IIhG6I\npEmcO/41XLvOzPG/w1O6rbba610mbHH2FFm7P0NysXCd/meu1pBAziNJS0iKQX19EXl+sa8YY3Vl\nlUZ1FsUY2nz/JLHvjhXNfXKeTuMsy8sN1uZPIMk6TStLq+dad2wBC1bmj4GkUG8ZNNqRUN/pPpN6\n8RbKDGBkd4hrpkRm07LBWtWmsvst1Ob/AddusriwDJJCpyaWDW7YZbKs1jKLi2vIsiYE/j1jSCBF\n+t/Aww91LEc8uwtnH8C1Fpk/f5ZOu0dntrSC3mO/4rtNAs9CMyu41jILsxdoRVX3VmuZpSWhs/Sc\nJmZ+D379JMuLc3huByMzCFRZXV2l7ZZZOPb7+L6Dqpdo1reupIUXIP33yU9+kk984hPcc889HDhw\ngA996EPPCKheqFAyWbz1alLt0StW3PY76Qz5m7bOHweB25f+i0t646oa0W+px3hug0tybC4a5+/T\nkW2DNjAYCamj8MSArO8dTX4VBiGS293eyfUzdCQVVYte2MifyyhOJmmui55nRK1frCHqpYRuDpOt\nXE+u0tUX9GqqlGxOVA5GoKpXkwaCeg59Gy0COY4vrDFS2jKSFJLlT1g6+Sfis3rcLqjTd63b609R\nm/+HZJu9Yk7PqaEaJfJDNyai+mdr6KYoMqUBHVlNiXYaoQDOKbM/FabrigAx40sMVVYTSwMQQnjd\nUBgY3pxKrS/+I6tRP63YF6b3GDPpDgRVFH2AVtNhZKI7UFaDkC+fHeCr999JrmCSH76FiSM/je8r\nFAsNpiYWmJxYxFQeYfbxj2A1Z5LtHrxyjP/wk7eRzuiRF84x2rWnSKUcJDlLeajfRTu1Y0/ybAKY\n+d047fmkJ2a6KJgl1RgQTV2dWiKKde01wjDo8xCqX5Gh7vQAJ99CVkwkxdh0j9bO/RVLpz6Z/Bzr\n0gA0XcaLmCrdjNPj4h2L9Y7VC19M9Ci6BLZvI0U2GnJUPWvmppGVFKvn/pp8QSHVA6p6tYPQrxkB\nKI9elQDWdLZ73WKxrdCVRKn5qDBEVkwCT0gGlk59oi/tF/bYNwSBm/wcp5a6bTRs1mb+lgtHP7xt\n9VPgW+iRL1ivHmWTVmRD6iQMA+pL/wwI/eMLHZIkIadSSLreJxF4psgOHKEwesemsSXWVCk90gYp\nKmgqvOgWzN17sE6fInPFETKHBGMVSz0kSSY3eA16aYg9v/E7ZA5fgW6I8ViqR8bIi/8k9qP3zytJ\nOxdJwY5Sq5cagW/3pdgkWSdXuZbC2EtQtTyttceYPforuO2uFs/3WrjWclKU1BuxYDyu+BapdQHI\n7eYMRmZi072Nq/2sxlmhW+v5e+81FgL1I0nFYZyqizW3qfwejMwkgW+xeOJ/cuHRX8ZpC72vavZm\nT0JcaxmreY764v19x9LXZ1cxk/RmrA+023ORj1jkSxlX7Xkd6ksPJn6P8TV17bU+EbpnrySpVjM7\nDUTvaugnGuPAs/DsVVxridLYXRjZyaQ36lbxr8ZSYavI33IrQafDyl+IwUopXPqKIQ6ns8T8sY8T\neB3CwEXueahiHVI8ufvRZ5LYIIKNQZWs6Xh2FfPALvQ3jqMcyNNeP5aUPku6DIGMF/bcuGaArHVz\n639z+l7+ul5nYOfr+/ahDV4agE3t209q/4FtjTIvNSRZoTzxcoxMD8Utq6j5PKm9l5HaexmSaiBH\nPki9oBS6ZdGxmHH/NTcQhDrlUo2J8SgfGAlyZSWFJGsEXjvRvgBUZ7+I3ZxJ8vi9oMp36306NCRl\ny75WG8NuzTL/1McSI8nQd0SPMjUDiMkrl+9/fTRDRVPFs5DLeRy+tntNJEni9e+4hqtvnOr7Tq/m\nIgzDZBDojWKhgYRFgHh+Yz3RsRM7+bNGh8WJE7zkTXuRJElMTLKG5ZQoFBqUSpGw1RYszOqZv+ib\nfGMtWVxR014/Rug3KQ5WSOf6tRbWyBCFW0TfQknWBTgI/USbIMSqYjBV9AKeU+uW8Yc+nrPeB6pK\nZY22K3x/avP3RaBKANdY97Y+9xVWZ/5GVPS1ZpOJv/cep1Jh4lRupuPqJzHxJeXfoZ8AHF2SaHud\n5JlUUuIZVLQs5clX4dkrjIxY7D/UfTd6izO01Cip/PYLl3y5+w76bp0wDLFbs+ipYcYO/iiVXaLy\nTYB8ATSdnvJx8b2uyL633YdrrYiWIT0Ve+2aKGbYKAhOvu/bqHoRWUn1g6oNlVdhYONay8w9+dt4\ndhWnPZ8Ah+00VX7g86uPfIwnL7FM/plCSaW39ZnbLvT0GIWR2zb9Xo2YKlXvgqr89Teij09QfvVr\nGHrLWzF37aLyprsxp6eZ+sAHSe3bv2k7McDLTFxJsGCht4cSrRJ0Rc5xiGdTwszt2tYzbbsIfKsP\npEmS2E5+6IbkngN0GqeJp2+7dR4IEwDVG+nS5Yxs0J2BKCxxrSWM7NSm78TPehjYScVgHHL0XsV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mhpwkZayEJiwzBLQrtFnuvQFi7rDJqLkQBVw5kqILcfmqaz4otRBxOGjrquIQpbCphX7iDx\nkSY+LO5FR/4CzwTbhNJI3lpE1x0BnHrLz8BvnQCyVDCMeT9QDW5lJwNVEpM13Z3DwcVnB7RTz3f4\nb73RjXqY6y3g7qn78K5H/uybfj5aV+0h2jwgr+ZNuqk0jRg7yudbeeR6xngP7DcZCzFLbChleOrr\ngSpvAzTNZPo8AgzWpeuFNU2DVQgVkpSkvQZ4FL8lB3gNpkoe73ny/Xhg7oD4d9GpuNRBIJOeS2X8\nRWhs/m6FeNBNpsUMu+cFwy0+E6Cqznsy9mFwEAYwuUrez9fFpmt/Sdhgy51EfeOrMHnlT4uwpqZp\nmLzyp7DWeN7rVD3XEfXnRBgCUMMiUaFnVRr3YdgNBN0ptBceQRwuC4+N6TA6MOwqkrCFUuMa4elR\nJs7imc/Ab5/Clht+Q2zGaw0FVKXRgF6IQlam3UAACNbJsMqIg0XWbmXxcZQaV8NscMNeBpYSxjCt\nRj24fOGq3jWbQMOyM+SJ7ycBXFNlFS50ZrDYX8b+ieuLP/2mDk3T0dj8msG/y0yVNjjlGptfA69+\nlTBEhlXlYatRZTORARnF+p3KDsWjHt/1Y2hO34Pu8gEBamSDE0NHEKo6HRK2x+EKknAVNV1HYrjw\n6lehNLJPpJXrNasAqhg4IT2WaYa4Zu8IMv8M+zuft4p2iQvCk6iN7vJB+O1T0DQTs5qHChgYt3i4\nzs+AJEsRYxcuTC+hXA1FL72gsMknF2lZUpt4GdzqFeLaS3rericOW2jO3AunshOWOwlN0zCy9XsH\njrHCQ3KrUQdwXOgmA1UZgPv8EK+FCdlXpmdeHruJ9VGTsngsdxK95iye5hXf5/uqON8Xt5LBcuTs\nH/ZOuisHoesWnPJ2RP1ZbNuRwTAyVBobMN9bxLnWFG4av0b6nQPdcBAACDINjtS82nIGQZWcJWd7\nGxF0zl52GQ6AsV6GWcmZqjXCf4AaUjCsMoLuOVicHeu3z8CyqixsygEwjZRXgC41rkXUn4HfOoEs\nDRkYlTYxzXDExkHhbYCBeWvzaxB0ziHonIPBQbhcpRvI51u/8By6zzNTNWycbU2hFbaxd/w6vPux\nW7HQX8JLNtyEud4CY0OexxY6xRFxpqodDgeNus6amFPpDmoBo8gWDHeNZtXMISO2XfSQXQdU1Te8\nEuWRG1jpGs4EGQPNpS9vEPO9FnikkYf/1meqFnpLmLNckMJqLaZqvSF0knz9MEH5BoXlowSXodfM\nf29atVy3ZnrQTBeIO2v+DmAAqr7pVZd3vZf17W/iYCJWtgmazjjvup2iOXt/oXFrgiwNUBndD6ey\nHe25B4UXGQVLmD/+t1g8/UnMHf1rLJ7+B9HtG2DsQH/1WeGl+a3j614XeWtCqF7UVBG7wgEBMQ+6\nWeIgb0H0cTNqdZijo9DHXSxzUHV09Yw4VnExbd3/Nkzu/umBa1JA1RCx+pfP3I1PHv3Hde+NxnRn\nFh999lOC6n6+Bz0zTTPXNIBOeauUPaihPHIDSo3r8gmv6UNr5Zh2XdG5mFZVZMnkoCpnr55cPIok\n8dFun8XS2S8g46n2dO4saqGmawg1G5pmoCEtKFZSoST+m5geCu+lcR8lyW5QA1sSzQNynaAMS2c/\nC799CrpZxvuP3Ykoy2C549B0A2EG+FkGP/Fhewb6vRDdToAqb249wFRdpB2GbrpwK9sH5lea+Oit\nHESWhhjd/oMX3ZxWglWYuokwy7P/Yumcp5pnle9rmo6t+34bo9t+EEAOXjTDgWFV0ZM24dOF3waS\nsSyKQg2riizxkUQdtuY0HRMTXXGO9x24A39z+OOIpf56MlPVydR7lMEeDeFRazqqE7dA022UeTjg\ncodh1ySbUABVkjOkScDfcsbV/mbRKmxvIyav/Cls3ftbyjHSuCd0g4ZVF/XuiuFY3XAFUyW3u9FN\nD17tSjE3DLMinrlcoqHE54ZfKMPQEZqq7jclHJdlGf74sVvx/qc/DABY6LNrXw2aSLNUgJ5v1qB1\n1S44YvIwzBKSuIuVC/+K7tKTANR5qxsWsjRUNL0EumSAK8J/a2iqxPmssihhoz0HpmrYINDcWQM8\n0iCt4lqaKnnEWYxeQu9HW7M47VrjsbkD+OKpfxnQVIlrkeyVbrjK55a3IWfVRNhQ0taapZypMtcG\nVc9lvGBAFZChz0EOTZg07qE5cw+vw5EXywPYQ3EqOxW9QewvDsSjUykbLwmb6LdPQjdLzLBzDUuW\nZUKbVBx5MTHWNXyt7DUy2kncBTQDOq/PEXTYhhF2z2Pu+Icw/huvB7QE8xxU+TLaLiwmnQvsikMG\nVUXGAmCGJ0iigb+vNb585m48OPMonlp4ZujnF9uwL2XItU0udYxu/0HUN75CLMRL/e1Xp+7HP576\nVwBMv6SbJWiSSHk56sLWNHSXnkJ3+QCCzjkkYVNot/S4jaquoQ/2G3kharoNu7SZZ7gwBnTm2b9C\nZ4mFntO4J2otAXmBuiwNBfCP/aUBti7TdCQA7u+Hooiqn2UIecaV7qTIMqDXCVGuOUM3k2Hhv+IY\nWBuJD79zDqY7roQDFnpLeNfX/0wU5kzSBM2ghV217YgVUJVfw5HlYygO3cgbTlPNF5uzYV0ptESA\nzOPGLZIyzQy7oazNg8IJyaBbFeiGK3p6We6Y8LiXglUhTtcNG+XG9ahMvBRNvtwSfh8E9qY7s3jn\nw3+Cpf6y2KhMqw7TaWDb/rdekkB92JA3O8Mohj90hFmGIMvQivP1Whm/CcVhlzYxZsJ0pTCKJsJO\nuuHCcseEvbJJd8PDlywdvYRival8fdHGVRJzQQ5NlXUCVaq9IYYqzpIBnd96I0mTNe0ujeOrwxOS\nZrq8Oe5zYBAvZ8QU/rtIWEw3S0jCFtrzD4tej3ImnqbbyJJQgCbTHkF903fCdEYRdC8I+5oJpurS\nN3kCw+b/JaZKv4zwX5Im6HEbwezwcLhx5+m78enjX1D+tuKv4m8OfQxfPnN3PjeNtUEZKxrMnlt5\n9EZsuuaXxNqT9VL59/Pw3+U870sZLwxQxR+2EOpy7YccAgS4fiDOhbJF3VHkLw54trGsZwmbiINl\nWM4YvPrV8NsnkWUZ7jp7L958z1uHsj5kpMjAFJkqp7wVI1u+T9SYQpYwRoMDJKpz5LdPI+xNY3Xm\nHgDA+ZgtpFACVesJFGnIC7xfLOCXZVjsLyFcg24eNjbwXmBHlgeZuxOrp/Er975NsAnL/srAdy42\nVoMmMg4iLgdU0aCFJJeyuNj4zPEvYoqLaWMOquRBDGHYYnOtu/wUsizmmTQG3LgJXdPQ5owG28jY\nMXTdhm7YGNvxOrHxRP6CMIZp0keW+MygaoZS6JI1m84QBUuwC2nMKQ+zHIjAwjhJhE6aIqEqwXYO\nXqpVd+jmdUmgSjMhb2Fp3EfQPQe3rIaYT7fOYro7izn+HFthGxky7KxtF06AYZURcw1Yza7i4OKz\nyLIMq0FzKAjvZCyLh8pu9KI+DA56TvPWFWX+nE3Jo/3KzBP44DO58PTB+bwJaqo7AH9GLImgghGX\naVwW+osSmHdgeRMY3fr96KfsCTwVJjiij4vreXrxEGZ787j94EfwbzMMJBuFQoPPZeSgXBtY31Ea\nI8gyNJMUK5LQ2ansgumMYS5OEPHnLetHeMs+GFZNhK51wxHsVHXyZQIsTe7+SYxu+0EhnC6GYMTm\nqJFgvaSErjL+jvr8OgbDf13pvy8vBPjeJz+Ajx759EW/8+A0ayNjcbtL/08MWZE5e74HOQ5rgY00\nS3G8fQE9STOl6Y4CAHTdRio5VrWNr0R59AZ4tT3oNY/jLx59N/zOWRFuXS/8J49cU/WN9WXMmapL\nDP9dglA9TmP4CZPMXCz0d2zlxEDx2LvP5YlnzNppQ9kxqrPFQBIvucAxQBE0mXYd5VFWOZ+6Y8if\nP1/jBQGqyrWt0HQLkT/Pi9Qx75GyyLz6VahwD150mE4TkeIIsOy7KFhClkSwvI0ibZRCM7pZQhw1\nEQcrMJ1RmM4IK8yXBvjaNPMuit5IlmUSqKKyB0WhtY7q5C2MmSLPWDMFTdpvHYPymLMEMFwspcxI\nBRJjfqmLSV7gRaaqG/fQj32kWXrJ4Tyde6+Hlo7iyPJxpFmKY4vMQ7zA+8jde/4BnGufx+8++IeY\nag+v3DxwnWEHb3/gXXiGg7XnBKqoUOIl/tbUTfFMszQcYAcuxHxH4u+1u/w0+53dgGk3UOXMZyvN\n4Ycc8qMxDACz9jkBdNNj2WaSF55wgWSWhnCrOwEAC7y1UN9im/dkibE5vdjHP3d9nHcY+Iqt/B1X\nas5QdjJJE3Sj3kB2ljw0TUMizcWgO4UsCZQMSwCCoSLwtthnoaitlU1YSTNcqO6DV79azK8bJ/Zi\nJVjFmdYU3vHwu/H12SeU42VZhrc/+Ae4OymjvpGFU/txD2Muz0ajMCo/nhxWfHp1CufaeauPtvRe\n/uHkl3G+x9a3U9nO9GBcODzfW0SHwKfEDPr8nYzXr8Tnl86gyUEJsWXnO9Po8bVJadffyKCsLN30\nBsKrYRKilWZYSFLFWdE0DbUdP4ovdn2spimSDApT1skyhFnGGZKcqapvfBUm9/wsRrbkmjjDqqAy\n/iLxb71QK0sXdbn64t+abubZzKXt+ES7j0d8xqStxVSx/+5iujM74OgNG1PtaZxsnsHhpaNrhg3T\nLMXhZdps2bMrW+rm7D+HBILLGeSsBEk4EHIHgDCJ0IojGFnONJrOiPKuqap4UTNVHrsJugb8iBVg\n/vjfiur1rcjHYn/thr3yEGHbbzj8R0zVOuE//dLCf1mWIc4ShEnIQ3Nri9SDJBxIdJCdjF6mwbDr\nQ+UJE1f8BCb3/Bx0w85ZO+7w5qApt9Wj21+HiSv+M5zy9m9tUOWUJkRvHtOqCQ3MfJNt7E5lhwBQ\npDW46/xDMKwqdJNV8ra8SVYULw1huROi8BrVs3HKWxEHK0iilpI1kSYBbI66i+xOlsUiC5HCjGsx\nJkeWj6PHQ24yUwVAZOGIGK6bG0iFqboEShVgYKXMWzAUjZy8GMNLDAHSZtYMW7j1wB249ck78D/v\nfjfmuvPiPMdWTmKZp9WvXqTvkTymOywsM8s35Uu9P3loGmstIzOEs935NYHdhDemaHKKi7mTZVjl\nbJUMymGU4GsWbLBnsRjngJS8rKnuvAhXFAGwptusYCLvXyeyzTgDEIctIWK13EmYzihOhAluW+3i\nKZNdBzE1/biP5TRDvcIY29DMQ2WVqjPUuCdZgn85+1XceuCOoc+FRiyFf0gz4xRqf+Wgis2ts20W\nft8zciU0aFjUXGiaLkDV3nHG0j618AzCJESTZwoeWzmJTtQVBvvR1SlR56sb9QWrJK6Hl8NIpP6Z\nC1Ff6GcAoJflJmvab8LhhtbjKc7EZMz3FtClljZS+NfnQLZWYmvQ5w7I6eZZbChNQIMmWJliS4xh\n4/DSUbEGDy8dHWBr8hDE4KYSJCH+sePjrn4g1haNxCxjKc0wG6eYSzVl/q+kGmNcdUvUkqJq024B\nIBcHzeXaxldi07VvFGCNogOUpi76nukWzsaJYDj92MfhpaPinXairgCynaiLdz3yZ/gA1z9dbDw8\n8ygANteK907jXPs8ulEPE94YS4rIMmGPaAyLLjyfQ3ZMh4nVwzQU84VGMbNO122kSSiyjsXm747j\nTBSjlWZYsXIN3NsffBd+/6H/fUnXZzpjfA/8xjK/yaasx1TpnIUjNvPE6umhOiwZjJru6IDGTx5B\nEqAX9ZVQsB8H2Fhmc/OsMYYNV71h+PUYDlyezLUeUwUwEsSrX8XaeQkB/LcgqKqOXIHy6H4ALJxC\nC//pGdaTR9cdIUKjOj9LYQ+apommjbrhsfBgGvJ2Jvz7PDOr1LhOAKSZoC+BKh+2bsPRgLCl6kLk\nlGLq6VcM/9E4sXoaIV9bmp4zVQBQGb8ZullCY/N3swbCUviHQFV2ERF3cbTDDiY8xmoUPbXFngSq\nhoQAm0ELTy8cUv4WpzFs3cJbbn4TAODYKgtZRmksQjmtsI1VngEmb+ppluK+8w/irrP3Dniosz3G\nmpR4SYPsOTRl1TQNBu/5R+NDhz6Kv37m74Z+3zPdNUEV3csFHnod2fb9KI+yqr0HWxfw9CoDD/Nx\ngvkof640H+85/wAenWVCVALNYZbheOPbURm7CWncF6CKEhcsbvRac18Tqb6m00Djyp/F/X6ITpbh\nfJcBfzJEfT7vJkvMEPX13GhVamuF/1J0o966RjEqaGo0zYRhNxAmIR6eeQxZlqEV5HXQAOBMawqj\n7gjqThVlq4QOJX7w5znKGacl3gKF6qe998kP4D1PvF9hgImR6cV9lKQN8vqxaxAlIfNw5X6IYHOM\ngF7dGxXvt5dmGDPYnHJrV7Jr4s/wwZlHxXpM+LpK0kQwVRp/p3GaYK63gF7cx/fs+E788St/H3WP\nPff1QFU77OC2pz6IR2Yfx+H5Y7jtqQ/iS6fvUr4jNJdDRLpBEqCXZQiywbA6hbXuDw18vqeGUx+L\nDXy03UcqMXDapW4MJNo1K7CkchJudRe27vttwaKS1iwtOJGnW2dx21MfxKePscKV3agnGFZ6/1Qo\n82Lj6MoJAcZOt84O/c7hpaPQoGHv+HXIwJiPIlM1jLXtRF0cWEMferlDnovtaFCsHiahYDYBFvor\nhvc13eJMlS++AzCQ9umOj9tbPTwRXJ4ejUZl/GZsvv5XvuGG17TWu1Hvojo3TTew+fpfRXl0H7Is\nw60H7sB9Fx4c+B49tzANMbn7vw7NKJbPnSFDT9pvgyRAw66h4dQx018aWoerFbaV92x7k0r2NGGG\ntUATgbBvSVClje6BW90Fw6ygseX7cGT1LAANJU6pakYeo/a5TmWZe4RjO34Y47tez8JvScArX9si\nbBQFi4BmcCaMGddPnblHZCWkiQ/LMLHPtmDM/ZsoxpgmPtrzDwEoNIhcA1Qt9BcFQCoyVU5pK7bc\n8BuojL8Im2/4NSSVK8VnIdfuxJfxKtphG+Me84aCgqe20M+zhoYxVV+duh8fOPi3YpMCWJaGqZvY\nVd+OCW9M+XsssXvBGhEAACAASURBVAYnuGhU3tRnunP45LHP4XMnv4SHZ9TWQtM8dEiAaGWdDX+t\nofMq8+yYs7xkxBKW+oP6riAJEQIgXCWnlVPY9FgUIzErsLyNGNvxOmzd91tYCjuivuPJKEFHCrFS\nWYeSpuHJBRYupPBfJ83QyxLoZokZzrjHqgbb1KqkhMndP4U06aM5828AmFA1yhhgAIA5Dj5jAaqY\n8a3aFZTNErroQOdC4UrVGQ6q0gRxGiPOkosmFoTF7DdnFJqm4b4LD+Hvnv0HnG6dFZlOAlQ1z2Fn\njW0UZaskMvfIi3cMG7ZuiQKhYRqKa5jpzinhatLt9eIeSqaH68auxs2T+7Gztg1xlvAyDRnaZh1n\n49y4E5vhmi46fBPrZxnu7wfQzbKg/GVmgdYjra0wDTGfpAh1FxqFt7JEiJ63VjajZJXgljajk2aw\nJB1TlES4/8JDildOm8BK0MQnDjKhbQaVtSBN1bDMJ9npWZW0n0DuLNXcOjqJr4TIurGPGEAsbaSX\nujHM8+KtzWRQGiCzrzlTpdo7mhPL/iqSNEE/9rGBg/8F7tA5xvqMdJiE2N3YCUu3cN/5hwayRwHg\nXPsCNpQn0XDYu4rTCEYBPAwLNf7lUx/CHQc/gm74jZd5kFnTYWL1IJGZKg1b9/0mapO3KN9hDH0m\nWg/Ru1oJVpCAsScX4ktPLFKOzRuof6ODwGmGbF1tnG7Y0DRNSEyGvQNyEMMkZCSDtnZVe3LSu9La\nYqWCHGwsTYr1WRx/9Mh7ccfBj4jfe/WrsHXvbwwwUGtlHX5Lh//+5cS/QdN0bNn766hN3oLbnv4Q\n2mmCEe6FBlkmWjxEwRLSLMNKpLZYoPYjWRpBM2zx/dhfhmFWoBuOqI66kqY41mLtLoipqvJNi8J8\nrbmH8kwOyWPVC55bmqV44MLXMd2ZzZkqzSwUHzTy9hmapoiKLe6tFxkEgAn4zhfCXGmWoh11MeqO\nQNd0nGiexkmpLMOi1MMuSgcXKk3QE1IRvyiJRdjk6tG8Sm2cqhu0zETQkBfUiUKmznSXhf9I2xKu\nk+mTH+c0ZrpzONU8gwudGTiVHaLFyuPzT4nvEaMmD7o2YjNkpqoVMLBwLEpwduQWHFhkhWY1Tcdq\n0MIzQYQgy3AgjBRtAWlSTkcxnl06xu+ZHb+TZgjiQFm4MlPFEio2ct1SylN5bUTShkp6AnrWxFR5\npouqU0U7asMrW7BsA7ZjrilUpzBumIaY687jifmnB74X8uumfYDAyBNz7Lvn2heU8F8zaGMlWMXO\nGqPYS2ZJGN2IbzimbsIzPcG2hEmkzHE5Hf18h81nP/bhmS7euP/n8d9u+CnYfL30+Hyaru3Hhdp+\nXFHfCSCv6J6kCdppil6aIQXwoB+hctUviPVFz2DUHcEYD/FF/F6DJMS5OMHM+HfAMB3xfXJMStyz\nHa/twG3NrkhWSNIEtx/8CD5x9LN475MfQI/bHvrddGcWRxZPiu/Kw7xY+C9m79HQDHFMGhTWGnHq\nSLNUhEYB5OeXqtKvtzH0oj4em30SXX59S9HFdU80L5IhdeUAwDUd9OI+MmSY4EwVSQ/sSwjzh0kE\n13Cwf+J6nGyexhdO3jnwHT/2UTY9WBw0RGk8UI9tWPjvbGtqzc8udyhM1ZAwV5CE6AlbUx7KGNEm\nH0ct5d9U+21LeSP6cQCvcS2LqPBxqfKN52PINuVSC7nSu4jTGAcXD2NJcujJuYnSeN0MTwJ0XSlT\n348DOIaD8dKY2HeUc6eJ0EOuWUNsiKZq+OffgqCq5Q/Sqr00Q4MDnTm/mdegiNrwMwYY5DAUe3B8\ncus2VgTaToXgtDy6D03NQ5ABB5dPAGBprLZhoUKgKsoLhdKQs4CKYuuTq6fxsaOfwXR3VmxYmm4K\n/VCqWQMGUza8lkmganC898nb8YePvkeZlCRCr9oVuIaDQ0tH8GdP/KX4XNZUDdt8Sch8fCUHQMRU\nAcCLJ/fnf09jxfMnZkg+LoUpNpYmcXz1lLjWLMsw3WEArs0NeJAOLq5jKycGGKePH/kMvnT6Lvzp\n43+JP3jkzzG67bVobPluAIw12VbdgopVxvGVYaAqwMs33yIWkqbnC6YptSn61LHP44PP/D2aAftb\nM2hiOknxntUuWmmGMAmFUbO9DTg29iospSwEcXDxsNCiPOiH6Ce+Iog3rBpMK2eqAAiti6hPM8Rg\nElNFwMIzXdTtKlaDFrySjVLZ5r8V+TDit0mWChA91Z7G///1P8EHn/l7RIXzhPwV0EZgOqNY7C8L\n3dSUAqpCzPcYM7y5zFLzy1YOqmhumLoJz/LQ5L8Lk1DZ/MjD1zUdfhwgTmNEaQxXMmY2X1d9blhN\n3cR/vvpH8Mv7mJaChKtxluBsnGABJl6/53UA1M0zTmNcNXIl3vntb4PHw84RNPSiHp6Ye0qcy+Se\nc5LmpQAcPmco7EoC/cPLR3F4+ShetvHFmO7O4uDiYRxYeEY8J3p27PpVsKLpJpzKzgHdGnsX7Lwj\nbkOp2wXkTBWxNAS00ywVwv5VJ2/ltV4SyKePfwF/c/jjONhZxEqSYjrsX/T7dnkLDLuO0BwugHZN\nV8yDmlVByfREDSn7EjLDwjSEbdh4w/U/iRdN7hPrUB4BL2xMDl+UREgLWa7Dwn80LgdUpVmKx+cO\nDAAA2f4NC62HSYh+Shmxw3VNtBd0OCNNLDfN6c2VTejHfUzs+jGM73q9+F2Pr4XZ7vzQzOznOpI0\nweNzBxT2U454rFdVXRyH2Kg0wvuf/jB+76E/Ep/JYHSYBlS+FrJ7nQGmyoVnDModVvxVfPbkP4t/\nd9cAgdo6oEn7VtZUyUiTXnQvy2By73Omt5yn1iOv7STTlHI2lqbb+MLpr+beOAdF1fEX43GLGbcT\nElNl6RYq3MNIwib89hlRBRlQM2+KlWvlMJoc/qMFdmenPaCziCXD4HCNQFAQO8rjSYlxIK+/ZlVE\nSro8FvvLIquqOJnDJBJswnGJ5YnSHFTtGdkttFUUTqJBnoRsyMj47x2/Tsk+60RdYfypCa9fyEb0\nYx/vffJ2vO8pVVwdJOGaBjFKmYd7RX2n8EjVewzhGDYybsgiydMeZrjJiMqfEViRQ4D9qA8NGhpO\nHQfmD8KwqljY/DqcjRP4BabKre0W4T/S0lDqL4VVioZisjSOtMBUlUwPY+4Ilv0VbNxSw8atdf5b\nHhqyq9Bp3qYxYh7S+epUno68WPDyAn6/KahO0yjO8NDLiNPAmdaU8FSDJBCbisU3ShlUkXNg6qZg\neejeZKZqlT/bCW8MfuyLd+vJxfoEU9XnxzTEd1zDEUxVnMaI69fjlTe/XYSq5VpFcRqL31IIPsqA\nTx77HD5zgrW5cgwbBp/vcZaIOexw9orCVwR6Diw8A8908Z/2/BAAphm64+BH8CAXW9MmpEEbGgrZ\nsOdnlAw8+TkBwKjTULx0IHdWSMxPx5V1J600RmXsZpju+Lp6TDrXk+053N7qYbqvzos0S3Fk+biw\nv6ZVxZbrfxWRlAqvSyyMazhinpStMspWKWeqCuG/c+3zykadZRnCJBJAumZXFYcnfwYBXMPJQVUa\nI+GOmcb/VyypoDig67Bx8jjVPIsPHfoYjq6cUP4eS3N8WFkFmalaC1QRQz+1ehyALurUrfirsHUL\nY94oWzMFlrMX9THTncM7v/4nAwko3aiHM61z6Md9UZLkUsehpSP40KGPKSHXMA2Fxm1ljcSB4qA1\nLjtuIoNXCpterGaibAfJrmRZxgC14cAxHMSSthcA/vXsvbhn6mvi32sxa7a3AbpZgbFGA2TLHWd1\n2by1RfTPZbwwQFXQwYq/ijAJxUvpSuK/Z1dPc/aHLUICVR0JVOkSI6HpFub6i+J7cviOvHn6jIkH\nNcFUrVz4V8yf+Aii/ixqk9+ObTf+rmhEO7rtB5SeZgDTU9BQw38uPo9NeCaMsVowGIkELjweHvAL\nLI48iZ5QQBVb2BW7MrDIwyTCatDE5spG5V5pzPcWkCHDxvIGzHTnBFsSS6AKgPjvtfQ58kIgD+fa\nUdaMgICOvLk0uVfcL+g4DvOCkcsFpirO4jULCcZZAlM3Me6NYtlfQT/uC2CbZinCJIJj2GKuRNIU\nH+aBkdFclUAVsQMyaO/FfZRMDzdO3IBDy0fhx76Yq36cF/wEqNZPnS1YnvVilzbBsGqi3lBUSCLY\nUJpUNFWGZsDSLYy6o2iFbdzyXbvwmh9grVcILNedGlzuTCRSQVBKKAAgmCYaJNQ2OXA0rAoift7d\njZ2YlfQLQRwK40gbGwNVvJcmMVWaAU8ClWFhgzjfmYZneqhYZfSTQMwNGVTRBkusrkm1zTQNI25D\nrLMkTcRn1J6pyFRZ/HODP5ugoBOxDVsArySNESQBdE0X7JUSbkoZM3n92DUoWR5cwxFzfLkATDZX\nNl5SOQEaBI4ZU1XIfhpgqjioku6jE3Yxsu212HTNL697rhFHtVtzhXnx9MIh3HrgDtx7/gGcap7F\nqeZZpFmqMI7yMVKeGAEAZbuEslUWIUo5/PfUwiH88aO34sOHPi7+FmcJMmQCSNft2lBHyk9YCIgA\nvRxK2lTeANd0BhJ1ZNa7eLw0SzHfY63DiutC9DAshJJIU1W3a4oDTSNIAqGpGlYraqm/jEfmWV9b\nMw2R6nlC0oq/ihG3IdZB8V66UQ93nb134JgA8PmTd+LPn3g//uzxv8KfPP6+dUNs7N7ZPZOjJZct\nCJIQWyqbYOqmkG2sNwjgykD/KGfUkktkqmQHncARvWfXcOByR0d+NqebZ7CncQXe/tJfV35XHE55\nK7bu/fU1W9GZdh1b977lopmJz2W8MEBV2MX/fPAPcPvBjwgg0JOYmxOt8zjVPCsy8Lpc69AO28Kz\nUjVMFhb6SzmokgqC0saTAoBmIk18JGkkQFUmbXZWaSM0TYNb2Y6t+9+GyvjNA9feDFqwdQt//qp3\niRpAmm6hHXZwZIVNsOICltmf0RIrvNmXJmGapcpE7UmGusWBQdXOvSJiVij2vKXMUvSLk5my8a4Z\nYUJ5AhlFUGXxDYeYKg0aKlLWzTCmalOFsXnkccrsQYvfS6/gsTzDNU2NgsGP00QR8coAi5iIUXcE\nYRrhLff9Pt72tXcCYO82QwbHcARzGUghsl7ch61biscdpRHT6UgAtcbDRvJ5e3EfnuVh7/h1iNMY\nJ5tnc1CVBMKgUnNoTTex5YZfQ2mENRrWNB2br3uz+FwO/5VMDzW7IsB2n+uNNE3DmMfm7oqUHUbX\ntam8QWTeJVmCmK+P1bAlnul8r9hXjxnC4xEHS+6kOO/uutpEOUgCcY+mAFVs84ySiAFcjekFZYAU\npGr471xrClW7DNd04cc++rwMABlMIA8Z0bw3pE4CDacuGjrLTJTYjKT1IYeyTbsOP80QpKlgb9m5\nbAHMqBK4I1d/J1CVRJjuzqEb9XD9GAO0VbsiNh3ZoTJ1ExPemGAZ1xtplgqHZMRtIEOmgADxmbM2\nU0WbSVEcP2zUbHXDn+stKBsxva9PH/8C/vTx2/Cnj9/GgVVul7ZW8+bmYRoJIFI2y6hImZzE9CVp\ngr9/9h9gGRaOrBwXNe8iPn/pndccdm1F0OLHPlzTgcnfR5wyrd4NY9firS/5VbiGOwCcKOmD/V79\n7N7zD+AdD/8x/v7Ip/COh9+tMN1kY4uMYZwyJ65mV4aWDgiTED5vKTWsj+StB+7AEwusdVDN0BFI\nZMFq0ETDqQuHpAjIe3FPMOiWZJ+zLMMzi88iTmMxF9erSfjwzGN4x8PvxonV04KJWpXmbxAH8EwP\nm0qT4j0VR5ImSsiQ5oYsb3mMN1BWEkYuUoh6GFNFe4drOiIkT+8ySEJc6M5id2OXmDdFYT3LIL78\n9kXPV5u2FwSomu8ww//s8rE8E0CafLrh4qHpR0Vs+lDGAMVtT31QdCqXU4r7acIn+xCmStrMNMNB\nmgTQ0lg0L5UHdagH1i4+SQvDNiyRjaPppjCudbuGue68gtwpzPP/3vRL+K4dr0IEA23put58z1vx\n7sduFf9W4t1885eNJG1ORL8TU1XU7cx256FBE+JfCm/FaawsWsFUcU/d1E2ULJWJoEEbWsUqwzM9\nNLkYnBaBbdiIMtYWpJNEyvOnKrrNsKUs1pizBzTkxU9MRbHGEZCDPcewRZy8L+03JI6WPek4jQdC\nDzWnMnCfxFQRmA2TUCxCP/ZhWGVsvv5X0JAKL2qaoYRl5IQFWXg87o3C0AyJqWLnAuRyBYOg6sev\n+mG8+cZfFM8l5NfTCbsYceqoWpUBQN9OE2QAjmgN3JmNw3QaYkO9cfIGbK0wJs3UmSBeZqMAoMzn\nQTfuIU5jAX5KkjcYFYTqzbCNqsU0gGuF/+id0MYig3zbsMW8ibNEhO7I4PaluRIlOajyxvbjr1s9\nBGkMXQqV24Ylrpvmmpyxlteti8T8pvVWs6sCjMge8qhXR8n0Lpmp+tV7f0eEI4kB6kkbej/xoWu6\nmG8+OSaRDKo6uHvqPvzhI+9Z93wy8KrbVcRprJRxGMZ09KT0+t988Ztw8+Q+8VmURGIzq9hlpdSB\nZbDnf7p1Dr24j9fv+SFYuoUHeHX0nNGy+PUwtl4GVSTOV8N/EdIshakbMHSDM1XDy7gAg6CKQBRl\nKcvn84cwgQAH6ZoxNDIA5Dbiw+1AtJiSx7K/ikm+pjxNgwwv2mEHVbsi1kERkPeivjhnxOt0AcD5\nzsyAzVqvByLp3Z5dOireuyx5CJIQjmljc2WTyNqWhx8H+JV734a7zt0r/kZrnICopVt4Yv5pNIO2\nQhxcrH2RbOe7oko+2XFHrEtRM681hTRLsau2HSXTgwZtgKk6tHQEv3X//zdU7rHWWOqv4C33/d7z\nol17QYAqmhBlsyQWXE/ok2yMeSNoRx1suOoN+ETfgOXmxdUom01W+K/yVFrRUmMIUwUAmW4hTXyY\nmfrS3dqVmNj9U6Jf2Wx3Hu9+7H1Dw0erQc4KCKZKM8V5KFV8vp8zBjThRtwGLN3E+eo+PNj3FRBE\nm6hruIrhaIcdaNBQtkp4x7e9FfvGrxcb34IAVZypKoT/pniK8igHJApTpQ0HVczzN0RhSqAgVE8C\nwf7UJW2EvBklAD7W7uNAEAmmrR8zgzHiNBClsWKw4jRWvI+mBKrYpmqIe5BHKDxgG4bpIc4y9KVn\nyhggD44kpI2zWAn9AUCVs04yqOpHDOgYWh42EqFk0dOrccm1xuRjj3ljMHRDAO9e3BehrbECqPri\nka/gzjNfga1bcE0XFbsMXdORZKlgqjJkcE0Xk6VxzElMVZqleNoPcKp8Ha4cuxbPNM/BjwPBVJma\nibe8+E345X1vwBW1HVxTlWtKgLyidTfqKQynHP4bpg+p2lW4pot+4gvg4cqgylDDf5bCnJriWctM\nlQgNrMFUOYaHbsaSDmTj7ej2gFDdkexHzlSF4ryCVbEHQzwAMFYaEfcnjyzL8N4nPiA8eBqiiKym\ni2PKc540JeTMFJmqhlNHJ+xivrco1j2NQ0tH8b4Df60AJfm/d9ZZ0oSsnZHZhJduZPqvOEsE4B53\nx+DKId40QifqwtRN2LqlFOWkzf/w0lHomo4XTe7DtuoWsVmLBBC+YQ5jquh9MaG6nP2XCKaZgfRi\n+C8PyRb1Vl4hDCQ7V/Rc53tLePdj7xMMbyIxVcNLKrB7aSYx0kIGd8i1hbsbeQkdOcu7E3VRtSpC\nj1gE5N1YrTtHe8rhJdZiS05UGZbpLQ+aY7O9+TVAFXMsNlc2ohm2B1i5Frfrnz95J/7o0ffiyPLx\nPFuZr9nv3v4qJFmCh2ceVZmqi4CqcAhTlb/7PPxHfzvTYvqxXfUd0DWd1c0rgKqz7fMIkhD/dOpf\n8L8f/YuhrYyW+iv4na+9UxARJ5unEaUxnlw4uOa1Xup4QYAqGhW7LBbc5tpOALxRqG4hSiLY3gYs\nxCEaQzZUGVTN8AflZxky6KI7O8AmHxnUTLOQxj4sXg+LUq+PdZfwz3OHhCE6snIcZ1rn8AyfzPJY\nDZqoE6jix9V0U9zHDl7fZ7ozg7vO3ov53qKYjLRBm94k+tnwLIYxb0QxHO2wg4rFNtJxbxRbKhuF\nF7PYX4ZrOCJkUJzMU+3z2F7dIrzftqSNUTRVFBrhJRUMzVAKNSrhvzgQm2PdqaHFF2q/4OFPJylC\n5OwYaR+ubFwBQE2ZL9ZKWZE0QnSto86g+FDO4vJGb8KdvUBhMSisJgtpoyQWxoWeW40/n2HhP3pO\nkVTC4LmkbtP8eP2e1+F7t78apmbmxT8jXzA/dacGQzOEIfy7pz7Dfi8ZUcZyxYrDwEDVhMJUxWmM\nCEDkjGFDaULoYmjjNHQDlm7ihvFr4ZiOylQRqOLgWoAqjUBVDpDk7D+a42mWMmYhDoSR8yQdWl5S\noaf8DmAgh+4tySRNVSE0wO4xkUAVZUtGyrs0dFPRDQZcEC3OJ5iqHDhbfAOurgWqvBF4pjugJ1vy\nl3Fs9aSiKZKHY9gCqMosFK0rypDMNVW8MKw3jnbUFRm6MtN7ZPkYnl0+poR+5XDsjirTiMrOBM3H\nV299Ob5z2yvYs5E0TLqmw5PCtcRUVawyNE1TmCo61+Hlo9hV2wHP9LChNCFYpKjAVJGNoHV4eOko\nHuJs0oBQPUvF3HBNd1BT5a9gnDvdxXUpv2P5OoCcJTrGbT0J1uM0hqEZqFoMVP3TqX9VdFuyLSwy\nMmTDXEmqQXtMlMbwkwAVHhZn18DeMYHGbtRDO+qK50TXe7Y1hQ2lCUyU8nBjNKTumDxoL5jpzoua\nb+QAEyvo6Da2cId8uquyVbI9nmpfwBdOflm8ZwKkW6ubUbOrWPKXlTUQJCG+PvM4nhpSkDW32bbU\nz1FmqlRNVTvs8DXD7FDFKiv1rYAcWD848yjOtc8Lp6Mb9fC5E19ClER4eOZRNMM2HuZ9Pqd4K6xv\nGaaKRsWqiIlz9QTTouh8UZFRDZJQCTXQkIttnmgymvd4mKDpboamaehFfcx25xGlkTAAiWYiSwPY\nHFQt8xYYh1szuGfqa8Io0f8fWT6GE6unhQFLsxTNoIWGw+jrVIAqSwiRaZKebJ7B505+Cbcf/Fux\neebeP5sgnSHVbMfcUYWp6nDKmIbQG2QJOmEHNaeahy+kRd4MWmiGbV6OoCKOBbAFrob/eGgki8Um\nVZKYKvm45FEDQM2uibR6UbywsAmRN0rMy54RpuM5tHQE7bCj0MbytdNg4T8DZaskjI18LQDfqMpb\ncDiMFUq9n/hwi6AqjQTQ28gbS9PGKXvvPc5UyQLnWMzJYF2haHHQsV+26cXYXtsKQzcEK8Suk6f7\najpG3IYAVZurGwaOZWgG0jRVQJVnOGg4dbSjDtPvJKHIErJ1WwmpiPkoARnHsBlTlRU1VSX+PHqI\nJAAjr8kgDYXe4meu/XHsHb8O3775JfAMF5GkxZE1VcRGDAv/kVOVZqkI/9B3LN1cM/vP0A0YmoEw\nzfu2XTd2NUacugj/MaZKDf+ZmgENGi/bQgCAXc9aTNVoaSTXxvDrmWpfEBWfr6gPbx9jaIYSUqXh\ni+wnG7qmS0wV+864N4p+1EeYRsiQKfOP1st5qWei/Dk5enJYncKrP7z7tSjzjL8kTSRQpSklMNh7\n7In5IDNVpL2Z7sxiV53VN9tQmkA77KAX9cW7IKF62SpB13RhG2576oP4zPEvAmBzhOZqnDD9o8pU\nqSzEkr8i9J1FUFVsOi47JpQpSPaLnBGWGMPCfxky3HnmK/gsD9uyY6i2UB4ENlwpY7zD9cBdKXOS\nnIt+3BdzHGDgIM1SjHC2WpRM6Uxja2UzNpVzecp6TBWBl7nevAAvBKppjjumI/arCx1VrF5kg3bV\nt4vnSaFlS7dg6RbCRC0aHSYhPvLsJ3H7wY8MZCrSM2s4DTHHaT1T5i+QS2DYXpXb/bJVHri2YncC\nstMfPvxx3HXuXpxYPY0gzcEcwDJUASahWewv4Wxr6jlrrF5QoKpkuWLBEbuk6TYsw0KURkzjkyUD\nBhBQSyocb53DZGkcR+MUZ20Wz77r3L14z5PvR5TGqNgEqgykiY9qFiHNMszznnCrKWUXspdFC+yx\nuQP48yf+SrwA5uUnaLiMqcqGMFUVuwzXcEUfvJQ3mgRybzwPqXQHYuNj7oho+0HXJBswSwnVMS9e\n13RYuqkYDbrm7dWtcAwbFhfT02+HZv/x8J9s+IHB8B9tjnWnilbA9FFk7OqOuglR+Qya+Hs4U/XP\np+/CJ47+49CJLFebjrMYBs+gGXVVtoqKKTo8a0SDpoj8+3Efnukq8yfOEmF8KdRWLTBVWZYJTRW9\nM6r+DTCjcjGKe9iIEtVbNzRdbI5MoJtvYCNOXbB1dZcZaFncb+i6IlQHwMEjCXxjPDj9CP7iwO0A\nmObFlDKqCMzJAn7HUJkqqwCqulFPCbV5lqqpomN6Vgn/Y9/PYe/4deKeSODtDQv/iZIKEqgyTL7+\nCfzlnzGxMnvHJFCVja5t2Ah4+O+qxm68cf/Pw9ANYTco09QxpUQXTWOOXBLlTJUQVatp81Qvbmdj\ny4Bw/o8efS8+e4LV0xnz1H5wNOR2PbKepxf14PJkBddwFKbK1i14picSBgBV1kAMxHlJG0P2w9It\nbKlsgmPYirNC4MAqsHhpSqDKQN2pSaG4iNsiZrvkRBZWpsJHkiViLZHDMtdbGACqFAIdpoFxjFyo\nHnLmjKqqO4XsvyzLsNxfxrg3Bku3BkoqFEFWlAwyVTSGhf9olNawhYNMVQ6caMym7NrJDlatMjwr\nZ6rk+k7E7JHUIUpi9KIelv0VbK1uVoD6egCgaJ+qdgVNbqtltqhmV1GxygO6qk5Ba+YYzgBItXWT\n79XhmnWqviJpsoD8mdXsSl64+SJMVZRGyvqu2IOgaslfUeZjyB0y0vAye52v6zRLcb49Lfai+y48\nhD9+7FZ86fRX8FzGCwpUyQUMbc6m6IYDW7eUYp8y05BkKbIsg6bpok5UL4lwRX0nKlZZaHh6UQ+d\nsIswCcUDZKnkdAAAIABJREFUj6EjjfvYpgU4FSVY5K0CVrghIdAx31tU4tfkUZGnRxsc9cnSNEsA\nGku3UHeqwsCVTC8P/+kEqvKNSiloqukCsNGEC9NIuX9VVJ576bZhKxqt8212/q2VTdA0DVW7IiYj\nlSmgIYCDEKoXw3+qUJ0mft2uIs4S9OI+/CSAqRkDrGJbMFXLsA1b9DAEWAbOMOOghv8ScY8Equjd\nkPdhc+/eKXiyIvwnC9UTJkbWoAnxe81WheohZ3NKlsxUJcpGVgxDrDdCHoamOSCHXIMkgCexAo7h\niE0vTmPsaVyB33/Zb4nPDY2xXMXwH91nmETKhkUeJTseuzejIKoXTNVA9l8h/FfIxAOY0aLnYRRq\nGwEMVMmbN50PyIGFOST8l19L/pmcVp9mKTJkCuhyDJtrqlTgJOpUCaZKDQ3Zho1QYaq4/qfAVNXt\nGt718rfj5dtfIp5BL+4PMJdrtQ5Ks1Sska4U/pMZcM90BahijoEHmzuaZGdkZoDe9ZTEVCVZAtuw\n8Yev+F1U7YqSUQlA1I3SNC1nqtMYKSiMy/Qrf/iK38WNEzcg5LrHoUxVlgjQQLaWWtnM9ebFXJbt\nWM2uDi1ZoDoHbHPUac5JgBpgDmeYRhhzR+EY9gCIKjJJChNdBFX9BfEMDM1QogOGNL+CZG2mit5n\nWfrthYSuNS+NQ2vdL9g/cuZHJaaKOhJsq2zBa7a9Er94w0/ze1mPqQpQt6uY5Pb2itoORGmEfuxL\nDD9bA1sqmwRT5ccB3nzPW/Hg9NeV4yVZIkoq0LAMCzZ35pOCbSQbLevGPnL4k/jokU+L50D2ltaz\nrKmivxWTqioFTVWSJlgNmnjF5lvwpht/gT+bUGix6FnRuRzdxmJ/GX4S4EWT+6FBw8FFlq1ZTPK5\n1PGCAlUxz9oDANsqQdMd1vKDe40yov6Nm9+I/ePXI0OWt+fImLcSgaWHV+2KaIJJtVF6cT8HVZqO\nNOmjrGU4FMZ4OgjwpDaCRGdGrh12EPEsmddsfyVes+2VAPJaJiKLiTZA/rI1fr0AM8Y1u5oXdLQ8\nyeMeZKpkUFUyPUk3wiZjlKhIXQ7jxGkiFryt24rRaEcdeKYn2ALSCNBv5YnKDKuJmFe7NbRcqF4y\nvYGSCjTxa3wTaAYt9DnbQpsR00ZYwngt9Vcw5o5A0zT84t6f4SDIHgBVtmErdK6sqXnd7u/HnsYV\nguHJPRx2Ts90FWNJDJCjhP9icQ+0Mbgm0051ox7unXpAhElLpieBn1ih3IeJIS82wiQU4Q8gB9hJ\nFnM9jVpuQK4p5hiOUrWaQlzy8KTvhGmoCKhtQw7/xRxUqabAMRyESYQojaFrumCx6LeduKswnAQM\nCJBTtpqsjaK5t+qvKqEk4OLZf5ZuIUMm5p0hfcbKNOShAfZbKdNPt3h1/FAB07lQnTNVhYKVFHIc\nYKo4qKL7Yqyoy8tK8P6kcZ50sq2yGZZuXpRJMHUTjmGL0F6WZVjhWcXiHnkZiiAJ4ZpO/rwK1e2z\nLMvDf51pIVUgLZIn9I91VVMlOWsiGUMSqhPg9kwXlm5zTVVX2FIZVKVpIjY6+nzMZRmuMlMl2zHX\nHN4o3DWcXMdYmKsVuwI/CcTxqKTMmDvCHarBulfyUJkqdf0u9pdFte+i/EHO0pTt9T1TD+CzJ/5Z\nFEAmB0F2LDtShi49H0M3YHPbKDtGecslzlSlkWhbtrW6Gbqmo8IBm1r5vYsHeaYlDUrG+M0Xvxk/\ndtUP48ZJJq9phi1lXwVY9vhMd5bLW5pIsxSnmmdhaAZ+5tqfAEBzo8hU2WJuRNJnzaAlQoSyo//1\n2cfFf1etirBhfjLIVIU8CiEXqgbY3tmNemKer/DrHfNGxfoJkxCz3RwgkT0A2Lwm52JjeQINpy5Y\nyrX0k+uNFxioikVauKVb8OpXwalsF0xVIKHLK+o7RJ860rQEHFABwJWNndz74W1nOJBJs1SE/zpG\nGZpuYzXVcCKK4WfAY72OoKrbUQeL/SVkyLCtsgU/sOt72O9EkTIyuOwlh5qDlUyH7W3MmSrDVLzb\nkumJyUgbVUXy/mXDUrZKYiMK1qA/ZYNDmXoAbcQqoyQLNat2WWTcFcN/7PmbiLOc/SLKu+6wQn00\niZnIlxtqO8/iofPRZmQbNkqmK8Dlkr+MMS4ovXHiBuysbedhI3Xz2VLeKIxlUVOzrboF141dze9h\nsN1IyfIE2KHWKJ7hFTRVHMQYLnbWdmBLZRPGvRE4uo17zz+ATx3/PL585m5+vFKeii+F/wCgf5li\ndbbJS8CIH7cX9UWtLRq2bot3KTN18m8HRLly1lQSKaCPMVWSpipNlLpQADOw5ITIrBGQG7JhQnWa\n6xR2lY/rSeE/mdmi7+maPlBRnV0vO0cOuCSgZuRp9UX9F6CG/+RnqmmaKGPhD2GqKOQo9D/8mJOl\nCWypbMJ1Y6zYrQx+c6bKF7bh2za/FJvKG0WfRACKqJyG3FORba6RKLUgM1VhGrL3x9dVlz8vctT8\nJECYRqjZVXSirvidHDYDgIZTKzBVoZgvOVPFNFUaNCU0bBsmwiRUmKpi+K9TqKdn6AZG3AaW+st5\nSQVpHcoZnvKQNVVUFJJAX0Ny4oA8+WXMGxVJEfIo/lvRVBWYqjRLseQvCzZ2ojQuElnkhIIwCcV9\nPDr3BL5y7t/wtQsPA8jF3WWrBK9+Dc6bowIoCdDJ9yLP9BSmSp7DOVMVY9FfZj1B+XOV1zGNR+ee\nxEePfFqtQ8Udh5Ll4dVbXy72uDOtqZzI4EB9S3kTwjRimXESe12xSrhl080oWyUk6SCosnSLM6ix\nwlTJRUbXqllVsjyEScSlI7lzbOlMziLCf0kk5j6Qlzih57ksgPWosK+MqVefBb37JE3ygtpWRXRp\nANS1fTnjBQGqZF2QXBhufOePoDrxUliGhVCqXVRsJxEkATOcWYYwy1Czq5jwxlGxKmJxyxsgsS7L\nRg3b9r8VnwpMeHySLvsrqDk1lExPpCwDrI2IYzgwNSNvKCsBQACA4eCLEWugK2e41CRdEUt/V8Mt\nhm7ANVxOX8tMVUkAIdq0mUcpM1W5boYy9ei8slfgJ4GykVWkFGE5Y4qGaXCmirNftIjH3BHWnTzL\njbgzhKnyE8q0y0GVZ3pis132VxVNlK1bCNNwQKi+qbwB/dhHL+oN1dQIxiGT5odB7FjOVIkq3pYq\nVI+zGD4Xhm+rbsbvvPTXeHgl/w5lfZZMT7A2xfDfxXqQDRvF90j30REiblVvJIf/iu2JDM0YqNcj\nh03keksA1z4UmtQWj0kgoxv1BuZGyfTQi/qI00SsXQoPj4q2KsRUSeE/Pk9Wg6Zyf/l12WLDkkMs\ndK10j4qmSto8h21Itgj/RUr4DwBPDoh5a6MCqNJZCJ88YwIVnunid176a7h6ZA9/Tvkx83pDOaiy\ndAumVC4DGF4TSq5UL2QF/FnKoCpKYtiGJVWgJ6aKp/ZzgEH1xijMlBbYyIZTRzNsiWsZylQVNEzy\ns+lEXWTIFKZKtEzKBpkq+k4v7ucsfsGODau15Bous5U8cUAuqVDn9oaeFzHao+4ID/+pa6K4RmWm\nStZejvONdb63yO2fAcew8b9e/ju4ZmSPwn4HSYhaoT0NJRx04x5PprAwccWP47yzTczRTtiBrumC\nxaJ3TO/xZqkHKzEuURKx968P2n/52VEYVS4BIYM/gIUPR5wGDswfHAjHUp3Dme6cum/yd2loBg//\nFUCVYXKhuqqpIvmGazgDvUhpOLotok6UOKJrOjRNY+9SIRXy9U2SDXr3tF+PeSPifoI0xGrYEvNa\nZqriLC/8XLUrGJe0j2td63rjBQGq3vYdb8Ku2g7OVKkCXoAWXDSAqEVmAPdEgyyDaXj473t/lgk8\neVo4oFYxt7lQmxZZksYi1gww8WCVF3sjo1WxKiJ1WGiRhOE0xf/T5I6EEM4Wxe3Yb2KlaCIN5jm2\nFHZpKFNVWFSy/oEyVei8sidG1Ynze2SAk7WiGARVFLJI+DGvHrkSP3/Df8U1vB0NXQ9lKdExAd73\nj6eE07siL4lqmgQFkEeMQpGp2sQX+KK/LFpGyEyFrI0JJUAOsMQHX2hRKI2/IFRPcqZKHrIBaoVt\nGJqBHTWWim5qhgj/ERj60um7BhpnX2yEiaqNMwSo6vHrlMN/uT5uGAA2eKNieXiGqqmSwxty+I9l\nVA0CNXpGw0AVAQCZGa1YZfzyvjfglVu+DUD+vJXwn/SMi0wVuy4rB06FOlXyMVWmypXYyET5Pt1H\nkHKmSldBlakZ4p0Vw382D2OEaYRilimQh3Rk9lcO/+XhfxOmpob/yMO/cWKv0MbVnZoARFRehJiq\nslkWjlyRqco3G3ZMOgZlcZGtSrJUKYBad2qKhx8loVg3mqaxOc5DPPoQUEXhHGKqTN3EG/f/PK4b\nvRpJmubhLVngzcG42MQLMoaYMxXycEwnTxxII8UBILBBwvxu1IOpGaJn3HK/iQPzed2hIAlw48Re\n/MINPy00cwBEcggx7Zs4i0PZr/JcLFmeEv4LklAJE427o2LP6EU9lM2ScJ5N3RD7QzvqomyWJLDO\nCsfSPnU9Z+CBfI7JIJ+GrKmlQc9erjVVrMWmaRpumtyLI8vH0OIFm+n90zvtx74CnCoyqErTAefA\n5gx4lEaCNXYNRzBVDaeuJP8ovxXlT0LR85GGYzhC2lFMRBkVoIqd49DSUYw4DcZU8WNGnKmaKI2L\nc5A9TdJYqf04LjFVl+so03hBgKobNlyNUbeBOIslul0FVTHXvgAQHqcjQBVjqmbiFJkzJtJ45Qw4\neXJYhsWFuDngkgXTFassWC46J9VoqdiygaNN3hLno8kdphE0MOMkh/9IF1AMqYy4Daz4q+Ka6nYN\nO2pbBzVVqUp/FvuU5dWmbcH6sd+rwKFslZionIotasNBFW3iVMRPPPM4FACRjuuZLnRNRzfqcU1V\nrutxBFPVF6nalsIokIZFBVWbedrwcn9FbJqypkZkcXFtjNyGxjVyZkzURhpSUkEuYUBDLhAKAFeN\n7BabpqGbLPyXxGKhnmyewVOLh3CpY63wX3cYU8WdijRLhzNVujGgF3FNuWeaCqoYe5JnVCVZOhj+\nM4mp6g4BVVL4T/rshvFrRbZnf0j4T37Gw5kqeV5L4T9DLbcgv/+KXUYrbPNnw9Z6kanqRb2BkCpd\nG7EKg+E/zlRJYTF5ECh05HuS1qqQMRiW0CfSIFB1RX0HJvn8GXHqWA7U9iEEGsp2zmJFSaQwVTTI\n7hDAIFBFgvEkTRVwRDaJ2Ay5wTE9G6pTpRfmm2x/5My2a0b3oO7UBFNl6ZYCVhlT1csdzgKoYptx\n/pxMzZAcVgtRQtfDmSpbZap6cQ+e5Ql240JrFnc883ficz8JULZKuGlyL2fG2XUESYAMGcb4hjru\nsv8nmyqDeAKGNMIkVCIR496YEKjL4VG6B6op1om6IvQHsLUhM5yO4eAlG27CrtoOZR0zkDdEUyux\nKgSU5YLKQaoyVQArLxJnCU6sngaQr79i9jeNMr9eQzcUvZ24FsNm3Q+kjGLKMgTYHkfPvBjqFaxS\nEvLIQW4fWIg/j9TI65skJMv+CsIkxLPLx7Bv4noBxAH2jppBC6NOA6ZmMBImlZiqsCOKKMtM1cUq\nwV9svCBAFUCLmIVUDCkrCshfNoEZMoAEroKYeaL3+yGC8W/Lf8fbW2RSGQOATUTKCgIY4CpZnkDi\nFbvCmKqwIzZjOudwpoomoyUmC8V+NU1TFt1aGpYRp4GVYFVc06/c9It47a7vyZvGct1YkiUKGBmo\nfi6H/2SmqgAcisUWizodFv6LkWTqJu6IkFKoZGkAzPspmyXOVPlwJbbE1m1OcffFu5CNM13vAFNV\nzpmqYssU+f4TnnUoG46SlWu45Ca+MmMxrPij/Hxo7J+4Pj+nZvCK6jHqdg1/9IrfA6C2E1pvhGmk\nCNXpPkTBwAKLB0ACuYV3pRkD52YsYV6vrAiq5IyqYSBfZaqKmqrSUFAlX+vQ8J/MVBnDmKrB7Dx2\nvQWmSrpW0n/M9xYFW6OAKt0WwME2i0yVKdmUIlNliuw62xgGqgaZKtKFRalcNJS1xJE3J8qaktfV\niDsisn9XgyY0aAL4VMwyIh6mDLmm0ipcrwBVA0zV8PCfI7H8wPCs4iRLhob/ZPAl9/xj98RC4x1J\nxE6jZJY4U6VmvgLgqfix4gjKgNXUzbzcjpZnnNq6Je65F+dFc2WQTDY8iPN6ZHLrox4vvUAbKrEV\nUZqXqRH3wEOYxLSESahkBnqWJ8KD3binlF+wdFMk1VARZxpUyFTuYPBz1/8XvOXFb1Qc57gQ/hoW\n/qN3LmfFyfcu7oU/KwJf9P7lTOR4DaaKWEwaRCBQggdpqjaUJgWr2XDq4v0Wsy3lfYU6X4jPTEew\nRnGqRmpKpgeHJzM9u3wcURph/ziz1ay0ENtXVnk2LWMoQ2Xv74QdEWVRmap/56CKKPJQoqFp0OZL\nkyQPKeXplnJtC/E7TlMXRcXMg1JDg4aWtz6pWGVR/6If+7D1vFdYxVK9Rna8wfCfHDaQmSoK0xXZ\nhhG3jlbYFpPNKoQ4/TjPclkr+y+RQkOWYQ2UPpA3tRyokjC48My5UL24iSvsIH/m8sZStsvoRl2e\nUeeKd+cYtuiNFg1hFCwOcuX3xDYW1htrSWKqlPIP/NoW+8t4fP4pUWuEXRczVFmWiew31/Swf/IG\n/Ied3/V/2vvSaD2qMt2npm8888k5J3PIQMIQEzKYANGEQSAyGBOBxLRBhTaAQ8QRBwSVyLo2jbq6\nFUXahr7t1bVczUL7unqJrQ0EpZ24tjGIIghREDInZ/rmr+6PqnfX3rt21Tl18p0x+/kDOfVV1a5d\nu/Z+9/O+7/N6Pv56hbWVBz1nk5PH+lnnY3XPOcI9q74ulJe5RfR8+COMEgWtyEyVITJVwjhmYq4V\npevYNAKmihdGFGKqhOw/R0xwqNdYmrr8/J5LReH+q3oxbvIxOYtPNMiDSf309gWQwb8DWVIBCBZH\n/v3P8Yv8vtT3stLoTltOwHDL7j/T4ozYcExVxa9VqWKqaLEMx2J5xhjPxkQxVZYZTL/k6jtWPI5j\npROekj5JrqRIHHjAZ5RSYabKN9qOFo8ja2eYgUAuIF6KwGu3WFNNZk5tX6aj5taV7j9CXjaqfGOs\nr9KP5pRkVPkGR6lWEjZUgPdOKz7bTODnK4d7j2TkGYbhxYZRFYdKQWlUDVaLQi1BegbadNIG4MyO\nxbhg9josnXam16eKUI2cnfXFdP0Cv/UyslYGVy24DLeu3oW8nWXuP4+pCvrA5jYHL/W9LIh3ZizP\ncFDOjVzCSaWmrtPKMz8q958qbpD+Tb+jdyKKPwfzMRnQlmGiXhdjqhxfO9CxbBYbaxkWY2IBL8Sl\n6sdiEdu3snsZ3nn2dsH9R9I3ct94zylu5Eiv8GjxOI74yulzmmey4ynLQaFaRF+5H63p1iBxhdyJ\nbKx6RtXsppm4aM7rMS3TkVh7kDBxjCqTjKpwDAMNInr5Ue4//hgQiMtVfCXe4O8OC2B1XZdRvB1M\n/DGPZqcJA5VBDFQHhcmeZ6pYoLoV0KY0CPnJuC3dynQ6mPaTbFT5mSUHfG0MmvTSnE6HnN5N96S2\nUFAl9c1AZYBN7oWamKZP11CJLVKfe5IKIlPFaNpqmXPHckYVx1SpA9ULIWOUnrcs0f9pKwXDMNCZ\n6cChwmEWU8VPctSPP9z/KOr1Gt686HLhfAqqp1iurJ1BT64LVy64jE3kcrwZ/5zTsp3YuuTNws6J\n3jMp0Qc0s0hp95X78b5HP4afv/IUZMgMCC0ULKaKZxXNgBpXGTKWabGxQYtclsv+ozgFgpj9FyWp\n4McjSHQ73YPicWQWKy0zVdxxXgeL6svxaPYXIAOGyGJITBU/Hmfke2AbFv7S/9fIQHXWNukdW6bN\nFsAo95/M4PB9AIhCkF5bHYGpSllOZEwV/xy0oTtWOoGjhWOCuCsxBP2VAeb+lzeedP3DxSOY5seT\npPyAciCc/ZfmFjGAmFPe/ee1uV5XGFWWmNLOwzJMP/tvgFVuYL+1s3DhorfUF5rj5Qxv79oiy0ML\nq8kZo61cFiO5/wAwfS0AohaTPwYo3MA77o3V1lQLrlm8iW2C2UZVYKqy/r0KzOhKWylsPO1izG2Z\njZy/4XBdF4OVQUFOgcblH479EeV6hWWQAkEdQ1lsl///wP0XPsaPLyo/1i+5/2Smiv7NmCp/nhG0\nD7n5mMmJKNx/LMub05SzTItl02WsDFtH+RjP82esweqec4Q5rsDVPvXaGSSjyIHqAHyj6pgyqzRl\npnCkcBQuXLSmW1jYD617lP3HZ6m+5fSr0J3rmgJMFWXi1MshapsWB5og0lzwMxAEqnt/CxsOfOCc\ndy+biRuSYKBl2MyoanI8958LF0cKR4UFt8nxKOy6G5QFoYXdMW3PRVevoVwPGLe8k8O7l1+Pha3z\ng0VMGhiUxXDA19OggeGYNmzD8oJflUH84gdAi9zK7mUoVIv4wYs/xvHSCZRr5VCcDhDo3ITdf05g\nAKo+YrfGPgx+4mhK5XG81IuqW/PYEu5dZe0MqzfnXUt0/3nZn4FhQn1wWutc/OnEi2yQRzFVndkO\ngb7lje4CV/ogeBZvYvWC7SWmym+3qiSS5/6r+bsmx/ffO5DjBEiKgddjIRDjwK7J3H9+TJUlZv8B\n8JWKw0wVv1jmnTwMGN6i6vdff9nL1Np42sV4z/IbkHOyoYwq2VDjv6OQUeVnz6oMrsCtHDaAAOD9\nK27Ened/PLRQA0F6uTwW5Zgq+f3PaJqOv/S9zL5xWVE9eKZwoHoQUyW7/7yxUa6FJ3HA2yjdvOyd\nWN2zQmwrnSdn/3GLk9r9533/hwqH8WLvn1kpGUAsYk0MuMye0Vx0pHCUxQY1cQK/MuPEx7AAxJyK\nOl7k/otiqkzDDLnNvZJJtVDMEOCp6wNeeRR5jiemnBa7zYuuwPYzruHuabOFle83PsCfd/8dHgyK\nTBeqBXaumqkKMoO9tlh+n1SYpAKB9KoGKwWWacbPOQGT5WWcCjUl/XG09/DvYBsWTm9byI6Ri6ui\n2BgE61g1xNSQy5mPHSYjsZ/F09WY8ceDnx/49vHiz9SeTQvfyDZC3tioC2OaxgSNoWK1CMewWaxy\n3smGDCe+z2WmKicZVXyilsxyklFVqpVhGqa0qXJwsOC9p7Z0C1Kmg75yH3NJVt0q+soDzP0X3DPF\n4q6SYgIZVV7wb0XymQKi+4/vNCVTJWifcEYVz1RZgfsvqMNnYXquG6ZhojXdwixXr0ixyFSRfg9Z\nzUF2R7D7l/WkzupcgpZ0Myd9IHY9TaqvDh6EAUOYyMnfrgrwFGKquFitMzpOx1mdS/CD/f+F3T//\nAoBwRhkAzt2ocP9J7BfAiVTWg7p6glSDk2PprTJTRRMepfyqJg6+/hm9yyXti1CqlfH8iRe98xQx\nVcVqMfQMzOiultjEKU9yqjp0fP/ITAT1AbmU6T3xAp2A9z6e8PVqZMMDQMjNHbj//EWeaw89B7ky\nZZaTfz+dmXa0pJpYPAEQ9HdHuo3pegkZVfWa4Iri76lqP+/yiYypqoRjqgAv4F8uLxRcl2I2ZNZU\nNqrE55/dNBN/7X+VY6pEHSvWtpD7z2buWRVTVal74p8qpgrwAvPDoqFR7j8VU8UzLh6b/f8O7EW5\nXsGS9kXsGNOxKw+wWE25TRRUfqR4jLn+mpx8sLBKWXxpyagKSXz4THUtJqaKiinz8AzIuiAMSsj7\n3//x0olIbwR9j7ObZgpuHD5bW+g3rt4o7/4jWQBAZKpoPJDRTMeBYANlGiZsw2IxVfwGOM8xVa8M\nvBq6F6uNWR0MJRXRxuWZI89ifus8MXHDSnvrCtvkisk4tAGSs9+o75hUAxdHRYyVSsEe4OeVIlPT\nB0TxZ/LwLJ92NtuUm372M29U0dgJPCBFj6nyiz7nnJwwH9O6E7hrfaOqXgkxVTkngwG/Lm61HnbH\nd6TbMFgtoL88EPrGefHoVj+mihe9LVZLKNaKQpYqnZckRpbHhDKqSBVbtkR59x+5hOjvBgx/V+AN\nnIzCcCjXRNl8CtTlU/ht08a5M1bj46+9BXknxyaE45JQIf19oDwAubgjrxlFejLCMxp2EPyoCFQH\nvCyGlOUIkxVRw3xGkXzPih9UzjMO71p6Hc6fsYYZP7L2EcC7/6IC1cW2siBGLnOQd43lnTxbqLqy\n00Smyp+QaBIUsv/83/H1z+j9LW5fCAMGnj7s6UWp3H9ekLpkVNlBMG6h4tX9E841bTbxyDtu+sjl\nmBE6j3Z/5AqRFez5QsVygc+6WxdqvnnXDHSqqFAwgRkVlXBGHX8uAFw+/w344Kp3e23y+4MywuS4\nMcpGIt008fl5pios/sn3hXhN38j1mUE5cywO5P6TGb/gmmpXdasvdKna5c9tns3+P6RTJai9R+lU\nqWOqosACrjnxX5LgIDDxX8nF2ZxqwrPHn4cBA4u5mDPq7+NlT5maTzQglGolvOwblsyoSuW5QPUh\n3H+yxAfL/qsJ7jZ6Rq9d4W/DMixf/T4cw5NjTNWJ0AJPfUybCtXxkhQ3SM9BmbGDfgkfAHjTwjfi\nrjfcCsBnqiT3HxnNQDAHii5+YupFpirLmKpB/HXgAEzDRLdfgod/xr5yv1caiF8fuI0j794FgvFH\nRpFc4YJqUcrMGfWNLCpqGxZ79yrCwXvGQH9N7m9yWatc6pZvONfrYaaKfavVImzTRnu6DaZhIm/n\n2POXqkHiDPU52+RXBlGpVwUPwdzm2SjS+FbI/xDb1VvuC2Vt8/3fkmr2japACJT+X47/40uD8egr\n90eWnCJMGKOKUvoHqwXFLoY+ONESZVpU1RKjd2WVXoDcf1yqLgtULwnFjS3TYrsOYqpcuFJMFQWN\nDvpxUyrXWJXpyYjPYXvZVlw5GULKcpjBJg/wrJNFf6VfyCgKnoV7RimrMGU5WNIeUMwZwfVFgzg6\npqpqH26FAAAgAElEQVTi1kLXDJgqtfuPn2in57tBxZtbUs3MOO0rkVHFu2lEI8+AwSaBJiePmU3T\n8dzxP3ltNfgP3Pt/F25ol8Izmf2VQaHUBD0zicPKBgfTuopw/zGdKo6pqghMVVCK6EjxmKDLUqx6\nKdx57to8UxWViViohbPf+HMBTyuM3BG04z7h97esDUXln2r1sPgnbVgAhOQ2eLeO7Dake9JELhuA\ncaDdokqpGVDHVAHeQubCZe+SH8skrwKE2ShxsZTL5ji+flEpkVFlE1PlL0YpYqoE959YpopArpL5\nrfOEBT5nZ2HAYPpVKSvs/nvoj9/H//rllwAEaeZeKSo+porPqA6MKlLHlgPVA/ef2E6eqZIhzz88\n6FvyvtWIjTNL1AgbAEEyRlhuY8CXzSBm2TFtLOyYB9MwUagWQ+4/EhsGgpgqmcWmdUOIqbI5pqr/\nVXRlpwlrAB0nl6S4AQ5+J483OWg8PB87bFzJ757GHBBIZHTnuti15EoT4n0pzES+poWKW2XjVqwN\na4ZiqlLMqArmccrwnJ7rRlumVVA4l70c1A7qN34+puSjZ47+QbhX8Axp9uyyW5lfS5udJsil0Oh+\ncvwfrw5AqNar+NhPPotv/eEhxGHCGFU0wRWktHgg+Dj7KwOh3SYZRyW/gK8qa4Kyptj1WExVOZjg\npMmf97GKAn+BxAHF1ATPQIHxVWXWkGMRpR5mqoBgUpWzlOY0zcR+oZxA2JAr18p+MVk5qzBwtYiu\nEJ9ur4bjm7xn8WQDZPaL+dvdWoT7z5to01YKbelWOJaDT675IM6buSbW/RfsVIIaibyR1OpnjlDb\nWDuFCVaOCwpcHIPVQSHw1XvmaKYqcP8pduOmlxlZqVeZwUGTNLF0NN66ctNYSQ8CyVjw17Y4pirU\nFsZUUfC3bMjw7Js85lKsiHWIqaKSEm44TsvT+kn71xTv15XtZDtc1Tjm2yC7juKgWqSBsFElxzhR\nPxIDyo/XuJgqembPgA+7/wB/I6eQVIgCBVyL7r9A86nOxaLIff7Os9+Km5e9Ezcs/ZtQO7N2Bsf8\nXbVjhnWqeHZPcP9FxFRZpsWM36CkliMcZzpVEF18QzFVQV+I/c3/PpTh7b9T+v7lc/lsZn5M0dgg\nlw6/CfLqMWZ89x8lMnFMVc37RgvVItJWKsRil2oeA8b/XXT/HcDMfI/yGYkBSSnWByD8LdI3r2Kq\nWHuljRyBd2XS+TPyPUwTkBg+lRubxn2IqTJFpkrsGytUpobFVHGJKtTO95xzA96y6Eohjo/Ik2BT\n6h1TvcfObAc6Mu343ZE/sLapnqG3rEiAII+D7ZUYk5+TNvGqIH4qi0Sg+ednr/wKcZhARlXAVIUZ\nnsD6lQ0OMo5UdHMQ4FsRB4Av/lmuldmOMrz7zbKdupyJAFBFcUmIk7ufSt+GdhRynBKB0k/lF7+g\nbT4GqwVWdV6Y/FgBWyo2G5ZqIKi0jwoVtfvPcw3VQpIKNmOqqkwXSpx0vEmlJ9fNXJhduU44ps12\n371K95/IVLWmWoRJOCO4o8Ipxd41opmqwQimigzVsKRCfKB6uVbxjFjaoVkOektett9//eUJNt7I\nNcC7APmaYPw1Ac8Yi2LNqG/CZUPCmXLsXNNm7j95d0wSIDW3HjLG+T6QJzHTMDGnaZbyGBAYt3LN\nuKEgBzYH1xMlFeQxToxfL2MHxDYtaD0NQNhwJoM4baVD7WS76ghF9ShQRmm5Xva0e0ybsdLve/Rj\n+Od9/4ft8MOyKm1YOu3MkGsI8AwkxlT5Ei+qOcS7jp9wk8qzjDpVwDnFlVIsoBiobjM2QlWmBghn\n/snPFMVUecfCRhMQxFSpjC62wAtGlde3VNstK32vWcvTqpMTmXimincbBu2xmQubZ2pprPSW+nCo\ncATTJaMqx+LG/LEosH8cUyUlxsjuP5kBZsxZvRr6xvmYPTq/x593+iuD7P3KhgMQ9HNUf7OYY2lj\nLZepYTFVZpCpS99pW7oVOSfHflOqllGoiFpU7D1GzFWnty3AH31Phfz8fBajKuEECDxP/LjrzLQz\nYkB+ft4ArNQquPWJz+AnL/8cw8HEM6oq4dgY0U0Ujn0hpirshw+C36qS/5euSR+bbFSYhsl2znyA\nd8YOFmo5vZPPxCurmCrDjmQGAAi+eR4LW+cBAP5w7DnWfgL524sRrqHWVEtgHCoyI1lMlfQR237R\nVMqMJIhMVTFkdFCfUcFOHjJTpcrSGqwUYMDAO8/ejk0L38iO88kCqsB5+XoAF4hZLWGgOhjaWcfR\n8bTARMVUMdaEi6miws/ffe4/2Hij8kdHOKOKsXE2z1Txu1g1a8aMKmlS6eH6OhSPaAWVA1RGVdX/\nNmSdKiCIQVKxUXP9kj2yWCsQTGRJXH8AQhk4fDsBXvxz+EwVALxn+Q14/4qdIWOV2qcqnCrESiZl\nqvzsP9tPYuH779eHfssWoyQsXt7Js3IfDlvAxHa1pVtxy4qbWH/RolWoFpSyGSm2sRTLOwFBNnYd\nCkV1ZlSpWNyw/Ao7j7v+smlnC8doc9IfEVPFG7am4punOUWej7KOqI2X4jZBfKC6fJ5jOkoj3jAM\n5OwsDhYOszR9HjmZqRI23dFMVVpiqpTuPxZTpXIN+qybH3fZ6bOVRcGgjGGqFC7Faj3QdxRKgxkK\nSQVTNM4K1WKkhl3ZZ6qynOeAEmuORxjHfHJLeJ73nsGLcVV7uZhR5Z+btTPI2Vk2N0YmOdVK6C33\no78ywMJPhsKEM6ooEJMH/+8wTedJ2Je4wr4EQadKUlTnA+qA8EIFBC9ClMzPsPNk/7ac/RemVD0x\n0nKtomQGiKkia53gFYfO4/dH/xjqD7ouMVWhunCmxT58odaeKS7UIbqZi2FQM1We+09eqGn32pML\nG1X0W3o+OfUVINrYi23r5EoGZOwIpipmZ5zhAtUHK4WQK88WxpU6UF3+uAEqCyOm9zuWwxioGqc2\nTIYyGVxA4HLl3ZFRGWtA8K5UiuIAcFpLEDckH+MnS1k2wgvGVutUAUGfqNio0/yUf74CffAsvlGV\nwGgA1MwH4E24lmFFZv/R4k7xEXJ7M3Yai7lsOgJtFlR1CGW1/+GCaZhxNTrl9jARy0TxZrlAGV5a\nwAidmXZBVJUv8KyMjfLTxhlTJQSqe4uqSqdKrhHHQ3T/Rffbyu5l4jWl7L+o9H/vHhxTRckYFDfo\niO/SY6qKbJNDfU4VHFzXZUksPGxh/pOMeDuLQ76cguyyTpmOH8cYZqr4/5fvR2tMf3nAy/aTsir5\nAt8qpoqMxoLv6aF2DVaLbI1Tuf9YxYtI919YU830a//J3h/+GVUhLiluk1uoFgTZBMB759Rv4XWF\nK/cjJyQJLn4140RrOf22NdUiVW1Qn1eulTFQ9cbkoUIg08FnWcqYcEYVoMjE4Sz8Zin1sSPTjoOD\nh3Fw8DAT0AvOC9ioumRVywVJVUZOEzOq1Ont8gDnJRzKCpEyeo5iraRkBoiy5eNvAG93NCPfw7Q1\nwm5FK3aipn4RxD+lGIbQ7seylbEftBDV6jUMSuUEAC+e45K5F+C1kn4PXccxHRY8q8r+UxXwBaKz\n0VQxdPI5xVrRUzeW2spfRx5Xi9sX4sI5rxP0gth5hsWMWCapYAZFZoEg4LQ5lYdjOqxoKRBkOAox\nVdz4k40L3uAEwu94rq8qDiA0GdPkkDKd0ELluXjDAocE5v5THFvdcw4unrsel867MHSMFsgo91QU\n4mKXqMQHoFrkiKkio2p496XfKY2qETJVrGYgFxogt+fPvX8BkKx/+EBa5rIx5U2EZFBwRpXKjUcs\nv6rAcZxOVWuqBZfNuwgrul4Taid/D9Uivm3JZty87J2KDNYg4JzPSmPXEuL0wu7u3jLF4oiGnsdU\nFVj2t81iIL22VX1pmJD7j2Oj5bUh5+Rw2N8kyYallzyVYWNRKP3DXSdkVDGmqj8UFwn47ki2qZAN\nAIcxSqRGzsqbVYvYe/h3yNoZJsTJI83NDzz47D/ZyCPdNV6mQ46p8n4XTsYCglp8fKUROpeMlVij\nSrFpYteQJRVM0ajiY2XjXNV8PC6tx3wIB2mUqTBxjCp+UbFlN03wwJ2Sxs2c5lnoLffhlYEDQvo0\nfx5f98w0TC9IUzqmWsgpxVtwPZkWUj4zFOX+o0BV1UAFPEtdZcSpBj2BH4AqJq8Y4RoBArkGuUq5\nYzqMNZGfP4oNYkyVH6gu7zZMw8SbF12Ozqxai8grHBrWxuLjhlTPwH84gjsyJtuIMth6S31+ZpB6\nXFFWCI+ck8PVp78p9AEDXt+QwamaTADgTyf2s982p5qE4qYDFVGjxXumaAPPMjyBv8BVq94BqkDt\n78x2KDSFyB0d1qkC4pkq27SxZdGVyvifgKlKZlTFgfpZFadFGV995X7WV8MBPZfM4AHiWErCVJFM\nBT83yOP5uRNeAdtEchPcmKCwBtnYkxlOkamqKWKqUihWi/jFq78GILv/onWqDMPAmxZuFJjk4Lzo\n7xEAXj/rPFYGhgevUyXHzXrPGvzNVASqn1AEOANhpopnlgFvrlZtDqPcf3QPMmJUbF3GzjBWUfRk\nOMJveNC3VlFIJtB1SFBXxVQxIdOaZ1TRuz9YOIxfH/wt1k5fFRGorh5LtunVq6wpJAyovmOtXucC\n3cWYKu93ata8WC3jSPEYy1Jlx7n2RXlA6HnFZwjLKBForNC4YEaVnRX6OWpDXqqVMeBvkPlN80G/\n8okKE8eo4tNSpSwtflLrkD5kqv3lwhV27ACfieANxrZ0KzPKQu6/GKYqlP5qpzmmKuz+o5T5sDK8\nd7xUKykZJWrva6adFTomTqph4yGOqZrTPAvTMh1KGr8c4VMWPg7umqZhwoDBSr+o3GNxyEr1vIL7\nec+uym4B5HRndfafvEsxDRMpy8FR30UVNqq8+3REGIBRUMVxyf33l/6XWfuanSahDtdgdRBpKyUF\n3EcbVYZhIGU6nE5VuH/OaD9d2VYaU/JmxGuzzVzjqvEfBKonM47ovSaNqQLg6/5MC/2d+koZGG/a\n7DlVcSNRoLGjYqpEd8Pwr+n4Cxyf/Su3+aW+vwJI5h7lN1UBU6VOzCDQt1msFnw3XtgY/9OJ/Xj8\npZ8C8BgogmVw2X8J2hmX/RcHXhpB6aZSJOcA4Zgq2cuR9YuqV1wxNohPRChUC8g5YfdfdLapWkKG\n3dNKB3VqI2KqwpIKqUDCRMVUmQ7LGlaXFPOer+gbiGREPHXgN6i5NZw747Wha3rtI6ZK5f6rqSs4\ncEwVlXcjA0m1HvLPAACHBo6gUq+E5l0yplXZuE0CUxXt/pMN+bIkJ0HvMudkY+Nx+UD1/qrkOYKB\nF/xNswrhGWqcIBhVElPFP7y8OMxumgkDhm9UyUyVGOD6hrkb8LpZ5yqPqRYOCpwN0epWxoupqlWE\n3QdzqVFGQQT7o5I+IHxpw+eUi1GzwFSFr8tiqhTXvXjuelwwe13o7ykzhQEMwjTMcBAzNwGoas3V\n6jV/V5TMqBJioyLoV9W74N+BJRh8HFOo2BlnrDSjbcPuP5/FiVD4joKKxpc/5gIXq9ecyrMgasBb\nOEJjnLumTIsD3rMVIpgqAHj38utR53ZSBFo8OjJhVoFJKtTryjEXx1TFIXD/Jd+zfXHDbra4CNeM\ncKUR8nYO5Vo5lI0VhyBQPWxUUXYjEP6O40BMFV9LT+5bVaziUFBtqqhPvMLh4fqVQ7n/eOPlU2s/\nLCQ8kIvHK8ScwKgagqmKAl/+Sh37wwWqCzFV3nl9lQGkrFTIAMxaGZbBZRpm4K5ioSFUwDe8kSc2\nOiMd479dVRxgWnBHhdcHIMyOehImKRRrpQimymaZ2uH4Vy5QXXL/HSx4jEqXglUEOPefIlOdYqpC\n878RGFWWYeH2tR9m792JmcdJxuOl3lcAhOddNp7tcDYu38+q9Y/aJBuHRSlIn1TSs3ZWqIca5f6T\n5XAc08Y5Xcvw1MHfIAqTgqniIVOGGTuN7lwXmpx8yBVBQoQsbdIMCsmSMRTH8JDKakht26/TVA25\n/3wXFtW2UwT/EaJ28Y7lKHeGLdykqnLjRFHVgJ9ZoZjgaCCpy01Et5XEL+UaTcMBLdTkhmVtidnh\nAJKkAu+OHCIwNm2lWSq6zFSRISOPqaHAt5sZVdK9i1w8RlOqibkDAPiaWWJb+HcuM1Xe9VNc9l/4\nHXvxauF+o3p4KncsE6OVimYToiQVhsLJuP9s01Y+H2OqFK5hIJgzZjfNGP69YpgqwzCw3M9Qkwtl\nx8ExHb/2W4kTRFS3OUn/8N9/4GoJMpmA8DxFC3ec+4/a1yNlHhNTUY9IYoiCqvj6cKAKBeAhq73L\n5w0o9N0AsAyzvkq/cq746m/+GS5cZaA6QZ7jaKylrJTy3WYi2HhBUkGRcUqGkJKpshylECf9OwhU\nLyJjZxjz1Vfuh23aSuFPYBg6VW7YHWn5tU9JNJhfs3ijRjW+U1YKL50go0qcd2k8qtzx+Rimij9X\nVWmAf05yoea46hq2ImSAj53mjaqcncV5M1YLIUUyJhBTFR1TxaNNSmEFgAtmn49CtRgyDABphy8E\nRsenaQPAkvbTsbxraUjqIGOlUaj67j8rbAzEZdQRki44qoWWv26gtj38VxqnjCzqq4R3HINVr6h0\nNsYAViGYOMLsl21YyjIE/HlyTI1hGGyXojIc01aKFdSUDRl6T+1SgsNQcITJUfTVp6wUavUayw60\nTHL/9cN1XRiGgQFFJiI/dlVMVdpKsWDcJAwHJQWoDEfbdGJ1qvii3knACu6OwP0XhaFkGmgHOjOB\nUQW/z6MW/61LNqPm1nFm5+JhX5KYk8FKgaWB898ksepAMvcoz1TL2X+eMKhaa40UxWtumI2khaM9\n0xaaOy2DYmrCbsM4DDf7T4ZKlZyHGKgejqkqVItoyobnMTK05ASYOc2zMK9lDvb7SQNhSYXwNy63\nL2qdigqcjhP/BIL3oYpVi9Lp8+4RCJkWqwVkrYxX7NpOe/2i2DSzcyOSHihQvVZXxFSZfpkaRSwm\n32+q5+DLxMg1QJdNOxvHSidwducZsc+vNqrSTDeRx5sWbESxWsLyLm+DtGH2+fhL30vYMHsdHvrj\n//Wup1g3WlItcEwHf+59ibldAW8snN6+AOdHuFOBiWRU8aUAYhZq1US0fvb5kb9PmQ7niuEDrilo\nLprh6cp1Yudrrgv9PWOncbR4PBSoHjaqwgHl8m+HC9VCy1+XshuTTNQ0mORikt6xeKaKmBfVLj8O\ntAtR78ZSqEYEqgeuqHC6sWVaqNVqykmc1zWTjSoyUlpj+lYFvj9ogqXnydletlGJc8c2p5pQdT1D\nK2tnMVgZxIwYN5XKgCZXHSAG6g8FUlNXuThTphdT57l4Ytx/Ce4HAClr5O6/KARMlXp800SdhKmq\n1ETtIhmt6RbcvPydSZrJ2jlQHUSP5bnT+PHS5ORZ0kKSjZXK/eeYKdhGoBItMzWGYfgbwKIyNooW\nfEpkEZ+DVLMTxlTF6FTFgZ8PVMkPYqC6OhZGxcYEca5FYc7tzHbgI6vei/c+6tUHVAWqE+Q5jr75\nKLFaoRwYt2BToWKLc0PyOOAHP6/qXh46lovJfrN9xhkI3H+AN9cWqkWWcKUC2ziFknyCovHyOLUM\nEy5c5TF+bl4lyWYA4kZedlevm7UW62atVbaTv64s7gv4LtdSODasM9shfMPNqSbcvPx6/zl8l6Vy\n3XBwducZ+J9Dv8XMfFAwO+dkYRom/ubMa5TtBCaq+y+GqUoKhzOqhJIm9LFFiGbGIWNl/Ow/uaCy\nyH6p/NSEpAtOnFGl0pEaDli6qeKji1ItB7w4IVoYRhpTpTIqA12f8DMQXa4yKOjdKZkqrqyRvBul\n99SiYD/jINL4YkxVxs54Y465lW22IJIhOlAdVJa/IaiYQ9E9Ovx3TOJ2qmB8vibdRHH/RSGIT1K3\nhZ5jBjcBDgVVLc2TBV9SKAg1CNrMG0dJ5gB+7AalPWw4lsPGg0rElMq0yAWVgcAIaUmFx79l2NzC\nmdz9R9piwz7PtFgsnSyoCcQxVUHfqpIUeCZLHseGYWCpz4rEhT+EmCr/241kqqQagsJ1DXvIOVOV\nqBTn/nL877jix/LRnET/VW2aCYH4Z5T7T5X9573XUq0c+45V8Y30HcssVRKoJXfU7r84sDiwiO9/\nRfdr0Fvuw/N+ti6gZlFD7Rt2C0YZQhCfYnK4dvGbY11gkde1HGUsSpD9pxZ4i0PaTjOROtmlZ8Dg\nSr+oA9WBaKHDKAznwwCSsQoBUxVuy/SmwOUpG5y2YTE/c5SvPgoZKQuDR3duGk6Ue2N1qtS15vxF\nRvFx0P2mZTpC192+5C147KUnMU9KcBgKgrqwlE2UtTIomkVOGdliCQ995QF057pQqpZis9RUu1hR\nUmL4i9W7l1+P/3dwr3IBEFnWRgaqN96oem3PClRrVZw9LewaAID3r7gRzx57Xjl3RIGYv6TuzThQ\nX1W5AsV83zalmgA/ETSJW012eQPAqu5z0JFpxx+PeUrPqm8xY2dQrBUETaGgrd795ZqY/LFyvZyQ\nqaLYGifS5RQFcosqjSpusRQD1eOZKn6jq9o4/82Z1+C7z/2HUHieP882wrGKzP0XsTEiNl6lt+WY\ntjL2CwDecdZbvYLqijHcxH2/8vdI6yKl+WdkoypmrRk6UF0RU8XGRkX5jV85/zLMaZ6pvN/5M9bg\ndyeewTkdYRZruIhy/wHJ2FGWCRphiC3tPANUyqs93YZjpePDIhEmjFHFDxTVR7whxsUXB8d0cKx6\nPHSPoeqJxSFrZViAO//BEb2r0mGSfxtVkiYKcRO/oHeU4Dnog1B9dAs6ApXuUAFf00Kx5KcMJ9zl\nxwVjLus6G388/ifmylGdp1rg6TlUH5TruuzaMnry3di65M0JWu/fz2+DOLmTAnsG/RUxc5KMVmL3\nouLG4pATJtXhv+N5LXOUAqZem9Vp6oQRSypQ9l+CrLGhsGb6SqyZvjLy+OL2hVgsLYxDgZiqpO8i\nDkK2F2UschsdnhUeieQEjyUdi7CkYxFeOPFnAOo4Hc8dXVS68YipVbGm1CflWkKjKoY1Hi5U7j9R\nUV1MVKE4NbmiBhDMM8VqUWmstaSacd1ZWyPPy9iZkHFI4SlRG2MyilRzo23akSETr50eFkwmxGW/\n0VpC8WFhpmpo91+coroq+w8AKrWy8j2/cf7Fkfe7YM46XLNyIw4d6ov8zVBQJwdE93kU4tx/gPfu\nz+pYgt8cfho9uS4cKx2PDU0iTCD3X+N2tTwc01GqpsvCoEncf/yOKOyLtiNF2vh/9yi0eEYK3npO\n8hykpNykqLlmGiaLteDV6OkeUS7OoZCJYT8o2+qVgQOhY46fNqta/O0YGvf5Ey961+5amqidcaA+\n5ilzPmhYzpzk3X91t466W0+8kPMTcZKYqjiILKtK/HNk7j8WqN5Apmo0wJiqkzAAZKgU/ml8moYp\nJHY0KuYsEDFVM1WB+098H0G5pOhyM+VaJVE7Vd9GUrQq3JFRkgq0kQUi3H9UjaBWTLTG2H7cjsrd\nQ/0VzVQRG6+KG7WVxu9QyDlh9y+BdN0Coyor/Fc1vxOiFdW5mCpF9h8QzVSNNlRGPj1HIp06vx/j\n3P8r/Lgw6uPhMFUTx6hq0EIhQxCNiwkqT7JwiHXoVEaVmqni7zESn/KZHYuxuuec0N/5Dy4q5kQF\nEkaL2sm87cxrkLJSoXRry7QYVZ80HoX6TrXb6Mx2YE7TTFw5/7LIc6PUvQG1gfemhW9Ea6oZC/yi\n1I1AoMkSXkCzdkZ0qxkWYwL7y/1MpM9RjPcFrfOwbuYa5T3FmJrGTGT85kDFmszIT0dXtjP0/ocC\ni39qYKD6aGDDLI/9Xtg6v2HX5MdgVmJX01aaCRyaEcHKcVg/6zzMaw6zjjTeVIt11s4wV738Ps6d\nvhoA8JrOcAwPjTEXLowRBKonWdxkqDK8BVZVYkDJIIhz/yXdyMhFqXk0O02Y0zwL8yPmlIwUZ8lj\nUev8EY23OKaqLd0Kx7Sxv+8lv80iUxUXqD49142ubGco/on6qlQrKcJYhhdT1WhctWBjZFJROoJx\ni0Pg4Yhew5ZOOxMz89NxducZmNs8m9U8jcOEcf+dLBUehagAX16o0zHtZEZVXCCi6aBXUaKAjhGS\nTqgA8N5z/lb595EuuBTEHPXRndFxOr64YXfo7yrGb7iIc/8BwMfW3BJ9rpVWx1TF0LjnzViN82as\nTtTGoRDEcKmYqqzwni3T8sVVs+irBEaV6jk+tOo9kfcUGY7GfCv8RK26Zme2HZ8+79bE16X+GY9d\nbBIs6ViEr1z0dw29Jv/uScaCmMW0lTopg3Prks0R94xmqnijSp5z5rfOjXz+kSbVNML9p0rKiar9\nByCeqRI2OMOf421ukyTDMi187LXvjzw32DiG+yAuaywOPCsmr1WmYaIrOw1/6XtZaHNmGO6/9kyb\n8htnWZO1cEm1wP1XGdE6NlJsPO0ibDztIuWxdAw7GIU4Dwcha2fwybUfBABleSUVJsxWcrReTpT4\nGgWVA+odXhyElFkFU8VYHCn1s5EBsTz43VSShYyYqqRB8zwb1kj335Dn2hnlxGjFMFWjAVW2IRlY\nGc79x7MRzak8+sr9qNTVAn5DYTSYqqaIgs4ni6E0paYy+PlgWpZKdxB7kx6VIH5mVKiy/6wMix0b\nabmZkUgqJI21VF2DhyMYVVLwN1OWj46pirpuFAKmKrmrLjOCoOnhXhNQGwF8jG4opirG/ReFKCkK\nIOj/cr3c0LjJk8FI3H/DYapGgoav8pVKBZ/4xCfw8ssvo1wu4+abb8bFF0cHro02UhEflVdQ2KvT\nlVUouMaBd//FxU2pajQB8dkYI8FIF1w5S2S4ODmmKtr9N+S5EVkzQwUcNhqqhYM+5hzn/hOKJPv1\n/wKmKllbR2o4x4GfbBtpAE0W999ogB/X0/wC6RbHJNFC29hi0x7TrgzgFWLxEsRGmSM0qmKSRgwA\nrPIAABdmSURBVE4Gcv1RHnHuP1vwVDTG/TcUmPuvgfMRHyyveg6+XmYwr8fracWB3n9RUaeWNO0q\nCp2q8ULgcm1sTNVI0HCj6t///d/R1taGu+++G8eOHcPmzZuHbVS9/axtmN2kTsUcKXgpApnlcEwH\n5XolURo2ACxoPQ0Xz1kPF24oVof/iOWPyrEcbFuyGWd2LEl0v6GQdUYWxPyupTuw9/DvEiuKC9IU\niZkqijNJPpCvmH+pUCmctcFPXR6rD5zGkSxY+JZFV2JF9zL84dhz3u+kIskHBg+xsjFJ2SbecE6a\nqh6F5lS8+2+kUBmVpwr4MUGbJ9qEpLmyJknq6Q2FdTPXRM6bvFGVROGef3eJ9KZOwqh63znvUn7f\ncW0D4t1/KSt6kxuHOPffUGCB6qPEnKuM5/NmvBaFahHt6VY27lZ0vQalWgnd2eRJUfxaGYqpGuHY\nGE2s6lkuJAUNB6O1GW+4UbVx40ZcdlkQaGxZw+/0uJTpkaKdS8+VFzIWi5BwN5K2Uthy+pXKY3FM\nFQC8ftZ5ie41HIw03b490zYiqQoajAaMRNmGAJdurFDFHQpLOhap22NaI9LFGSnq8LIhedrYMAxc\nNHc9AHVMUVOqCc8df4FjqhK6/xKWAxoO+IWvkdm3zii4uCYLeJc/jUcStkzbqVFhqqbneyILSacF\nKYIkTFW8xE0UTsb9d0bH6cP6XVj7yWeqFJtjgalKouF3UkxV8vT+JFCNne7cNGyTYu6aUnm8Ye6G\nEd1DEH9WlCkjTJQM37Z0Ky6YvS7ROSymagRrUex1G3o1APm8n+nU349du3bhlluiA495dHUlKxUy\nXJxWmQk86/1/T1crmtOBJZtxUjhRBlpz+YbdP5/NAMe9QdnTHdZbGQ3kysHAbuQ9o/okn/UnDTuF\n7u5kauRNFW+iac7nGtbnuUwaaTs1amNIRqbo63tls8p7NuX8eoC2w45PP9CBgZcHkW/xnr+zrTlR\ne418UNR3NJ6zo62pYdc9Am8M5nOZMXsnEwFdXc3Icxpl/LPblo2WXB7T2r3vJWXbY9I33eWAhW5t\nHv43dxjB75rzw3+PJL/Smm/cnCqjp7tV2EA1ZTPACaC7ozV0zxndbUzHqjnBeDxuemO4u70t8XPU\n694a2Jxr3BzHI+mcOxJ0loN2tzSJz9FRC9bQfC49omecCPNC+4D3HO3NjZv7gFHK/nvllVfwnve8\nB9u3b8dVV101rHNORgwsDmYx2KkdP1pE0Q7oZRO+fkzNbtj9/ZhQ2EbjrjnkPTkdqUbds6urOfJa\n1bIfiD+CZ6y7dRgwUCnVG9bWWsWFNYb9ffyEJ3waNW78+H+YrsmOm5UUXLh48cCrAIDBvkqi9har\njX/HPPp7SzhkNea6A71eB1RKtTF7J+MN+l6IiQTE92QbFlA1Uej3EhUMbmyMJkr9wbgZGBj+mOs7\nUWb/XyxUE7XVNEzUyqM3px8+3C/8u171DKxivzindHU14/DhftimjUq9gkrZHXab+vu8568WRvYc\naSuFWmV0+mAsxs1AX7CJKxfE77i/t8T+fyTzeNzaMpYY7PfnqcLwxwWPKEOs4UbV4cOHcf311+P2\n22/Heec13tWVFB1cvJDs4ojTdxkpguC3sVOrGMu0VmB4qahRMA0Tb1qwcdhU/3Bw/sw1WNKudg2O\nBpZ1LcWG2etw+fw3KI8HiuKckrbv6z9WJHX/ZLT5yej+xIF28Un0zYZCEKg+MVwDYwnbtHHF/Evw\nmmmigv/l8y/BvOY5qMfUWhwN8LUvk9Ua5GKxEs4vVy24DGe0N+77HgpxgeoA/FIjlUTf3Ix8Dy6Y\nvQ5ndY4s/vXK+ZdiXsvcoX+YAB9c+W7s7/tLQ68ZBYfrK1XtV/b/k/gbp+dodOxbw1f+r33ta+jt\n7cW9996Le++9FwBw//33I5NpnOGSBHFZUywYMWH9ujiwCvJjlN4/HqBg+JH6oi897cJGNgdndixu\n6PWGgmPauHbxpsjjNAkJgep+8OixUrhk0nAwWvFiWTuDwWqhoZl6oxGMPZlw+fxLQn+7aM7rAQSq\n12OVGSkIvCa4J19kPKlRdem8xn7fQyEuUB2gObmQKKbKMi1cE/ONDwWKr2wkFradhoVtpzX8uirI\nVSF48ONookgqjAT0HI0mQBpuVN1222247bbbGn3ZEYNfjOSFaVSYKmvsmaqxBitEeRKlKKYyogLV\nAeB48YTwm/FGYFTpQPWxACvhM0YaXryhkSSoOCMYYxP7PdIGNo6pAibONzcZIASqh3SqJl7230hA\nRnaj5T8mr5nZAIyGUeUw999UZqrGVhdqssFRMlW++2+ETNVogZjcmu+WagS0URWN1Bi7RnlDIwnj\nZBgGkz8Z6/CCpCD3X5Q0jk2FrU9BMdqRQpBUiMn+m8zfuHUSYSxxmNhfS4MQ1WnOaLr/JsiiORoI\nat9po0oFlVGRc7IwYOAYMVWjVOsyKeY2zwbQYJ0qy4FpmKMWBzaZ4ZgkqTBW7r+RxVQBgY7ZRDeq\nMlYatmFFzrkpzVQlBm+My0yOwFRNYkOV2NiRaJHF4ZQYZZ8+76M46gcI87DZDqfxgepj/QF/5ryP\noeAXch5tBJXoT4nhkxi0I5a1fjJ2Bv2VAeE3SXD7uR9BS2saqAz92+Hi2sWbsLzrbMxubpzormPa\neN8578KsphkNu+ZUwVgzVSPVmwI8xf1DhSMTRhl/9/mfQG85nKW1fvb5WNJxeuTz0Tw/UTYykwEd\nmTa846y3olAt4pyupcIxfux2+VUDJiPmNM/Czte8HYvbFzb0uqfEKGtLt6ItHdZvGlX33xizOFRn\nbCwQMFWaiVDBjnB/pa0UM3xHYnT35LrQ1dbYdGTHcoZdKDQJGj1RTRWQq2o8XFFJDbmmCcZUtWfa\nlNUfmlNNsUra49nnkxWGYeC101coj/HB6ZN542QYBpZ3nT30DxNiYnwt4wTKXmsk/Re4/6auayxg\nqqbuM54MVDFVgEipa1fEqQnLtMa0pBKPpMYRZaxSQebJivHyHkxV8GO3hyvkrOHh1DaqyP2XsKBy\nHMZDp2qsYZ0Cz3gyiJrE00JZGN13pypSZmrMsv94JE1/p4zV/vLAaDRnzMA2OZM4qHoiwY7RsNI4\n5Y0qYqoaGag+9bP/aHLSgepqRBUU5tPU9QR/6sKx7HGJU0rMVPlGVV9lkhtVLPtPGwCNwGTO+BsL\nnNJG1cymGZie7xklSYWp+wGbWqcqFpHuP1/d2jasMSv+rDHxMK95Dmblxz4WJalRtbTzDAAIBSpP\nNmidqsaC+nHTgjeOc0smJk7pUbayexlWdi9r6DXtUyimaio/48kgCFSX3X9p//gp/dmd8rh5+TvH\n5b5JGYbuXBe+ctHfjVJrxg6OqZmqRsI0zCkxLkYLpzRTNRo4FZiqIPtv6j7jySCQVAhn/3l/1/2m\nMXYghmqiSCOMNbRRpTGWODW/slGEPU6SCmMJnf0XjyhFcc1UaYwH6DudKNIIY42oGEcNjdHAqfmV\njSJOBf+9dQoE458MhpJU0EHqGmMJmouS1P6bStBMlcZYQhtVDcapFFOVmsJs3MkgKKisllTQk7vG\nWILG26maGhEUuT81jUqNsYU2qhqMU0FSYbQKUU4V2EMxVdqo0hhD0JxUbWDR7MkEzVRpjCW0UdVg\ndGTasLhtIU5rmTPeTRk1TM93Y2HraZjTPGu8mzIhkbOzWNp5Bua3zhP+rpkqjfHAtiVbMCPfg45M\n+3g3ZVwwv2UulrQvQmuqZbybonEKwHBd1x3vRgBoaD0zjZNHV1dja8xpAL85tA9f/+3/xqK2+fjA\nyptHdA39XiYm9HuZeNDvZGJiqryXrq5m5d81U6WhMUYIAtU1U6WhoaExFaGNKg2NMYJ2/2loaGhM\nbWijSkNjjKAD1TU0NDSmNrRRpaExRgiYKp3araGhoTEVoY0qDY0xQtrWTJWGhobGVIY2qjQ0xgja\n/aehoaExtaGNKg2NMYJtWDANE47O/tPQ0NCYktCzu4bGGMEwDFx+2iVY0rFovJuioaGhoTEK0EaV\nhsYY4o3zLx7vJmhoaGhojBK0+09DQ0NDQ0NDowHQRpWGhoaGhoaGRgOgjSoNDQ0NDQ0NjQZAG1Ua\nGhoaGhoaGg2ANqo0NDQ0NDQ0NBoAbVRpaGhoaGhoaDQA2qjS0NDQ0NDQ0GgAtFGloaGhoaGhodEA\naKNKQ0NDQ0NDQ6MB0EaVhoaGhoaGhkYDoI0qDQ0NDQ0NDY0GQBtVGhoaGhoaGhoNgDaqNDQ0NDQ0\nNDQaAMN1XXe8G6GhoaGhoaGhMdmhmSoNDQ0NDQ0NjQZAG1UaGhoaGhoaGg2ANqo0NDQ0NDQ0NBoA\nbVRpaGhoaGhoaDQA2qjS0NDQ0NDQ0GgAtFGloaGhoaGhodEAxBpVlUoFH/nIR7B9+3ZcffXV+PGP\nf4z9+/fjrW99K7Zv34477rgD9Xqd/X7//v248sor2b+PHz+OtWvXYseOHdixYwf+5V/+JXQP1fX2\n7NnDznnb296GM888E88//7xwXr1ex+23346tW7dix44d2L9/PztWq9Wwa9cu7NmzZ8QdM5ExFu+F\ncNddd+Hb3/42+/d3vvMdbNmyBddeey0effRR5Tlf/vKXcfXVV2Pbtm3Yu3cvAODpp5/G1Vdfje3b\nt+POO+8U2jdVcLLvZXBwEB/96Eexfft2XHPNNazveBw9ehTXX389tm/fjltuuQWFQkE4dumll6JU\nKkW2Uf7N17/+dTYONm3ahHXr1jWiKyYMxvud1Ot1/O3f/q3wDfF48MEHcc011+Caa67Bl7/8ZQDe\n/LV7925s27YNW7ZsifzOJjPG87184xvfwJYtW/CWt7wF//mf/6lsX9w898tf/hIbNmxoRDdMKIzF\nOyE8+OCD+Pu//3vhb4VCAdu2bQut9XG/UbV53OHG4N/+7d/c3bt3u67rukePHnU3bNjg3njjje7P\nfvYz13Vd91Of+pT7wx/+0HVd13344YfdzZs3u+effz47/6c//an72c9+Nu4Wkdcj3H///e4999wT\nOu+RRx5xb731Vtd1XffXv/61e9NNN7mu67r79+93t23b5l5wwQXu448/HnvvyYqxeC9Hjhxxb7jh\nBvfiiy92v/Wtb7mu67oHDx50r7zySrdUKrm9vb3s/3ns27fP3bFjh1uv192XX37Z3bJli+u6rrt5\n82b3qaeecl3Xdb/whS+43/3udxvQExMLJ/te/uEf/sH9+te/7rqu6z7zzDPuww8/HLrHnXfe6T70\n0EOu67rufffd5z7wwAOu67runj173E2bNrkrVqxwi8Wisn1D/Wbnzp3unj17Rvj0ExPj+U5c13Xv\nuece9+qrr2bfEI8///nP7ubNm91qterWajV369at7jPPPOM+9NBD7h133OG6ruu++uqrwvWmCsbr\nvZw4ccLdsGGDWyqV3OPHj7sXXHBB6Ly4ee6vf/2re9NNNwltmSoYi3dSKBTcD33oQ+4ll1zi3n33\n3ezve/fuZdd77rnnlO1T/UbV5vFGLFO1ceNGvP/972f/tiwLTz/9NNasWQMAWL9+PZ588kkAQGtr\nK775zW8K5+/btw9PP/003va2t2HXrl04ePBg6B5R1wOAV199Fd/73vfw3ve+N3TeU089hde//vUA\ngHPOOQf79u0D4FnLu3fvxtq1a4e2KCcpxuK9DAwM4H3vex82bdrE/rZ3716sWLECqVQKzc3NmDt3\nLn7/+98L5z311FN43eteB8MwMHPmTNRqNRw9ehQHDhzAypUrAQArV67EU0891ZjOmEA42ffyk5/8\nBI7j4IYbbsC9997LxjcPftzz1zNNEw888ADa2toi2xf3mx/+8IdoaWlR3nMyYzzfyQ9+8AMYhoH1\n69cr2zZ9+nT80z/9EyzLgmmaqFarSKfT+MlPfoLp06dj586duO2223DRRRedfEdMMIzXe8lms5g5\ncyYKhQIKhQIMwwidFzXPlUol3HHHHfj0pz/dqG6YUBiLd1IqlfDmN78ZN910k/D3crmMr3zlK1iw\nYEFk+1S/UbV5vBFrVOXzeTQ1NaG/vx+7du3CLbfcAtd12UDM5/Po6+sDAFx44YXI5XLC+QsWLMCu\nXbvwzW9+E294wxuwe/fu0D2irgcADzzwAN7xjncglUqFzuvv70dTUxP7t2VZqFarOOOMM7Bw4cLh\nPv+kxFi8lzlz5mD58uXC3/r7+9Hc3Cy0o7+/P/Qb/r1QW+bMmYNf/OIXAIBHH31UcJFMFZzsezl2\n7Bh6e3vxjW98AxdddBE+//nPh+7BvwP+euvWrUN7e3ts++J+c9999yk3L5Md4/VOnn32WXz/+98X\nJnwZjuOgo6MDruvi85//PM466yzMnz8fx44dw/79+3HffffhXe96Fz7+8Y83qjsmDMbzW5kxYwau\nuOIKbN68Gdddd13seXRuf38/PvvZz+L6669HT09PYzphgmEs3klrayte97rXhf6+atUqzJgxI7Z9\nqt+o2jzeGDJQ/ZVXXsF1112HTZs24aqrroJpBqcMDAygpaUl8txzzz2XMUaXXHIJfve73+EHP/gB\ni+HYt29f5PXq9Toee+wxXHHFFewYnffVr34VTU1NGBgYYOfW63XYtp3w8ScvRvu9qCD3+cDAAJqb\nm3HjjTdix44duPPOOyN/c9ddd+G+++7Dzp070dnZOaQBMFlxMu+lra2NsRIXXngh9u3bh1/96lfs\nvTz22GNC/w51vU9+8pPYsWMHdu3aFdvm5557Di0tLZg3b16SR500GI938t3vfhcHDhzA29/+djz8\n8MN48MEHsWfPntA7KZVK+PCHP4yBgQHccccd7J4XXHABDMPAmjVr8OKLL45Sz4wvxuO97NmzBwcP\nHsSPf/xjPPbYY/jRj36EvXv3Cu9FNYc5joNf/epX+MpXvoIdO3bgxIkT+MAHPjBKPTN+GO13kgRf\n/OIX2bm1Wm3YbR5vxFohhw8fxvXXX4/bb78d5513HgDgrLPOws9//nOsXbsWe/bswbnnnht5/m23\n3YZLL70Ul19+Of77v/8bZ599NjZu3IiNGzey30Rd79lnn8X8+fORyWQAeBbpv/7rv7LzHnnkETz6\n6KO4/PLL8T//8z9YvHjxyHthkmEs3osKy5Ytw5e+9CWUSiWUy2U8//zzWLx4Me677z72m3379uHu\nu+/GDTfcgFdffRX1eh0dHR343ve+h7vuugs9PT248847I10ikxkn+15WrVqFxx9/HEuXLsUvf/lL\nLFq0CKtXrxbG/RNPPIHHH38cW7ZswZ49e7Bq1arI633uc58bVruffPLJKfk+gPF7Jzt37mTH//Ef\n/xHTpk3D+vXrhX52XRfvfve7sXbtWuH3dM/LLrsMv//974fcwU9GjNd7aW1tRSaTQSqVgmEYaG5u\nRm9vr/CtHDp0KDTPLVu2DI888gj7zbp16/DFL35xFHpm/DAW7yQJhmO0qto83og1qr72ta+ht7cX\n9957L+69914A3u539+7d+MIXvoAFCxbgsssuizz/Qx/6ED7xiU/g29/+NrLZrNLNdOutt+JTn/pU\n6HovvPAC5syZE3ntSy65BD/96U+xbds2uK6Lu+66a1gPPBUwFu9Fha6uLuzYsQPbt2+H67r4wAc+\ngHQ6Lfxm6dKlWL16NbZu3coyNAFg3rx52LlzJ7LZLNauXTsls2dO9r3ceOONuO2227B161bYtq2k\nz2+++Wbceuut+M53voP29nbcc889J93uF154Ycpl/REm8jv50Y9+hF/84hcol8t44oknAAAf/OAH\nce211+KOO+7AtddeC9d18ZnPfGYETz6xMV7vJZfL4cknn8S1114L0zSxcuXK0Ngfzjw3FTEW72Qs\n2nz//fczMmY8YLiu647b3TU0NDQ0NDQ0pgi0+KeGhoaGhoaGRgOgjSoNDQ0NDQ0NjQZAG1UaGhoa\nGhoaGg2ANqo0NDQ0NDQ0NBoAbVRpaGhoaGhoaDQA2qjS0NDQ0NDQ0GgAtFGloaGhoaGhodEAaKNK\nQ0NDQ0NDQ6MB+P/0aFoAOhitPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115007f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_random_series(df, n_series):\n",
    "    \n",
    "    sample = df.sample(n_series, random_state=8)\n",
    "    page_labels = sample['Page'].tolist()\n",
    "    series_samples = sample.loc[:,data_start_date:data_end_date]\n",
    "    \n",
    "    plt.figure(figsize=(10,6))\n",
    "    \n",
    "    for i in range(series_samples.shape[0]):\n",
    "        np.log1p(pd.Series(series_samples.iloc[i]).astype(np.float64)).plot(linewidth=1.5)\n",
    "    \n",
    "    plt.title('Randomly Selected Wikipedia Page Daily Views Over Time (Log(views) + 1)')\n",
    "    plt.legend(page_labels)\n",
    "    \n",
    "plot_random_series(df, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Formatting the Data for Modeling \n",
    "\n",
    "Sadly we can't just throw the dataframe we've created into keras and let it work its magic. Instead, we have to set up a few data transformation steps to extract nice numpy arrays that we can pass to keras. But even before doing that, we have to know how to appropriately partition the time series into encoding and prediction intervals for the purposes of training and validation. Note that for our simple convolutional model we won't use an encoder-decoder architecture like in the first notebook, but **we'll keep the \"encoding\" and \"decoding\" (prediction) terminology to be consistent** -- in this case, the encoding interval represents the entire series history that we will use for the network's feature learning, but not output any predictions on. \n",
    "\n",
    "We'll use a style of **walk-forward validation**, where our validation set spans the same time-range as our training set, but shifted forward in time (in this case by 14 days). This way, we simulate how our model will perform on unseen data that comes in the future. \n",
    "\n",
    "[Artur Suilin](https://github.com/Arturus/kaggle-web-traffic/blob/master/how_it_works.md) has created a very nice image that visualizes this validation style and contrasts it with traditional validation. I highly recommend checking out his entire repo, as he's implemented a truly state of the art (and competition winning) seq2seq model on this data set. \n",
    "\n",
    "![architecture](images/ArturSuilin_validation.png)\n",
    "\n",
    "### Train and Validation Series Partioning\n",
    "\n",
    "We need to create 4 sub-segments of the data:\n",
    "\n",
    "    1. Train encoding period\n",
    "    2. Train decoding period (train targets, 14 days)\n",
    "    3. Validation encoding period\n",
    "    4. Validation decoding period (validation targets, 14 days)\n",
    "    \n",
    "We'll do this by finding the appropriate start and end dates for each segment. Starting from the end of the data we've loaded, we'll work backwards to get validation and training prediction intervals. Then we'll work forward from the start to get training and validation encoding intervals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "pred_steps = 14\n",
    "pred_length=timedelta(pred_steps)\n",
    "\n",
    "first_day = pd.to_datetime(data_start_date) \n",
    "last_day = pd.to_datetime(data_end_date)\n",
    "\n",
    "val_pred_start = last_day - pred_length + timedelta(1)\n",
    "val_pred_end = last_day\n",
    "\n",
    "train_pred_start = val_pred_start - pred_length\n",
    "train_pred_end = val_pred_start - timedelta(days=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "enc_length = train_pred_start - first_day\n",
    "\n",
    "train_enc_start = first_day\n",
    "train_enc_end = train_enc_start + enc_length - timedelta(1)\n",
    "\n",
    "val_enc_start = train_enc_start + pred_length\n",
    "val_enc_end = val_enc_start + enc_length - timedelta(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train encoding: 2015-07-01 00:00:00 - 2016-12-03 00:00:00\n",
      "Train prediction: 2016-12-04 00:00:00 - 2016-12-17 00:00:00 \n",
      "\n",
      "Val encoding: 2015-07-15 00:00:00 - 2016-12-17 00:00:00\n",
      "Val prediction: 2016-12-18 00:00:00 - 2016-12-31 00:00:00\n",
      "\n",
      "Encoding interval: 522\n",
      "Prediction interval: 14\n"
     ]
    }
   ],
   "source": [
    "print('Train encoding:', train_enc_start, '-', train_enc_end)\n",
    "print('Train prediction:', train_pred_start, '-', train_pred_end, '\\n')\n",
    "print('Val encoding:', val_enc_start, '-', val_enc_end)\n",
    "print('Val prediction:', val_pred_start, '-', val_pred_end)\n",
    "\n",
    "print('\\nEncoding interval:', enc_length.days)\n",
    "print('Prediction interval:', pred_length.days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Data Formatting\n",
    "\n",
    "Now that we have the time segment dates, we'll define the functions we need to extract the data in keras friendly format. Here are the steps:\n",
    "\n",
    "* Pull the time series into an array, save a date_to_index mapping as a utility for referencing into the array \n",
    "* Create function to extract specified time interval from all the series \n",
    "* Create functions to transform all the series. \n",
    "    - Here we smooth out the scale by taking log1p and de-meaning each series using the encoder series mean, then reshape to the **(n_series, n_timesteps, n_features) tensor format** that keras will expect. \n",
    "    - Note that if we want to generate true predictions instead of log scale ones, we can easily apply a reverse transformation at prediction time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_to_index = pd.Series(index=pd.Index([pd.to_datetime(c) for c in df.columns[1:]]),\n",
    "                          data=[i for i in range(len(df.columns[1:]))])\n",
    "\n",
    "series_array = df[df.columns[1:]].values\n",
    "\n",
    "def get_time_block_series(series_array, date_to_index, start_date, end_date):\n",
    "    \n",
    "    inds = date_to_index[start_date:end_date]\n",
    "    return series_array[:,inds]\n",
    "\n",
    "def transform_series_encode(series_array):\n",
    "    \n",
    "    series_array = np.log1p(np.nan_to_num(series_array)) # filling NaN with 0\n",
    "    series_mean = series_array.mean(axis=1).reshape(-1,1) \n",
    "    series_array = series_array - series_mean\n",
    "    series_array = series_array.reshape((series_array.shape[0],series_array.shape[1], 1))\n",
    "    \n",
    "    return series_array, series_mean\n",
    "\n",
    "def transform_series_decode(series_array, encode_series_mean):\n",
    "    \n",
    "    series_array = np.log1p(np.nan_to_num(series_array)) # filling NaN with 0\n",
    "    series_array = series_array - encode_series_mean\n",
    "    series_array = series_array.reshape((series_array.shape[0],series_array.shape[1], 1))\n",
    "    \n",
    "    return series_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Building the Model - Architecture\n",
    "\n",
    "This convolutional architecture is a simplified version of the [WaveNet model](https://deepmind.com/blog/wavenet-generative-model-raw-audio/), designed as a generative model for audio (in particular, for text-to-speech applications). The wavenet model can be abstracted beyond audio to apply to any time series forecasting problem, providing a nice structure for capturing long-term dependencies without an excessive number of learned weights.\n",
    "\n",
    "The core building block of the wavenet model is the **dilated causal convolution layer**. It utilizes some other key techniques like *gated activations* and *skip connections*, but for now we'll focus on the central idea of the architecture to keep things simple (check out the next notebook in the series for these). I'll explain this style of convolution (causal and dilated), then show how to implement our simplified WaveNet architecture in keras. \n",
    "\n",
    "\n",
    "### **Causal Convolutions**\n",
    "\n",
    "In a traditional 1-dimensional convolution layer, as in the image below taken from [Chris Olah's excellent blog](http://colah.github.io/posts/2014-07-Understanding-Convolutions/), we slide a filter of weights across an input series, sequentially applying it to (usually overlapping) regions of the series. The output shape will depend on the sequence padding used, and is closely related to the connection structure between inputs and outputs. In this example, a filter of width 2, stride of 1, and no padding means that the output sequence will have one fewer entry than the input.      \n",
    "\n",
    "![1dconv](images/Colah_1DConv.png)\n",
    "\n",
    "In the image, imagine that $y_0,..., y_7$ are each prediction outputs for the time steps that follow the series values $x_0,...,x_7$. There is a clear problem - since $x_1$ influences the output $y_0$, **we would be using the future to predict the past, which is cheating!** Letting the future of a sequence influence our interpretation of its past makes sense in a context like text classification where we use a known sequence to predict an outcome, but not in our time series context where we must generate future values in a sequence. \n",
    "\n",
    "To solve this problem, we adjust our convolution design to explicitly prohibit the future from influencing the past. In other words, we only allow inputs to connect to future time step outputs in a **causal** structure, as pictured below in a visualization from the WaveNet paper. In practice, this causal 1D structure is easy to implement by shifting traditional convolutional outputs by a number of timesteps. Keras handles it via setting ```padding = 'causal'```.     \n",
    "\n",
    "![causalconv](images/WaveNet_causalconv.png)\n",
    "\n",
    "### **Dilated (Causal) Convolutions**\n",
    "\n",
    "With causal convolutions we have the proper tool for handling temporal flow, but we need an additional modification to properly handle long-term dependencies. In the simple causal convolution figure above, you can see that only the 5 most recent timesteps can influence the highlighted output. In fact, **we would require one additional layer per timestep** to reach farther back in the series (to use proper terminology, to increase the output's **receptive field**). With a time series that extends for over a year, using simple causal convolutions to learn from the entire history would quickly make our model way too computationally and statistically complex. \n",
    "\n",
    "Instead of making that mistake, WaveNet uses **dilated convolutions**, which allow the receptive field to increase exponentially as a function of the number of convolutional layers. In a dilated convolution layer, filters are not applied to inputs in a simple sequential manner, but instead skip a constant **dilation rate** inputs in between each of the inputs they process, as in the WaveNet diagram below. By increasing the dilation rate multiplicatively at each layer (e.g. 1, 2, 4, 8, ...), we can achieve the exponential relationship between layer count and receptive field size that we desire. In the diagram, you can see how we now only need 4 layers to connect all of the 16 input series values to the highlighted output (say the 17th time step value).  \n",
    "\n",
    "![dilatedconv](images/WaveNet_dilatedconv.png)\n",
    "\n",
    "\n",
    "### **Our Architecture**\n",
    "\n",
    "Here's what we'll use:\n",
    "\n",
    "* 8 dilated causal convolutional layers\n",
    "    * 32 filters of width 2 per layer\n",
    "    * Exponentially increasing dilation rate (1, 2, 4, 8, ..., 128) \n",
    "* 2 (time distributed) fully connected layers to map to final output \n",
    "\n",
    "We'll extract the last 14 steps from the output sequence as our predicted output for training. We'll use teacher forcing again during training. Similarly to the previous notebook, we'll have a separate function that runs an inference loop to generate predictions on unseen data, iteratively filling previous predictions into the history sequence (section 4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Input, Conv1D, Dense, Dropout, Lambda, concatenate\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# convolutional layer parameters\n",
    "n_filters = 32 \n",
    "filter_width = 2\n",
    "dilation_rates = [2**i for i in range(8)] \n",
    "\n",
    "# define an input history series and pass it through a stack of dilated causal convolutions. \n",
    "history_seq = Input(shape=(None, 1))\n",
    "x = history_seq\n",
    "\n",
    "for dilation_rate in dilation_rates:\n",
    "    x = Conv1D(filters=n_filters,\n",
    "               kernel_size=filter_width, \n",
    "               padding='causal',\n",
    "               dilation_rate=dilation_rate)(x)\n",
    "\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(.2)(x)\n",
    "x = Dense(1)(x)\n",
    "\n",
    "# extract the last 14 time steps as the training target\n",
    "def slice(x, seq_length):\n",
    "    return x[:,-seq_length:,:]\n",
    "\n",
    "pred_seq_train = Lambda(slice, arguments={'seq_length':14})(x)\n",
    "\n",
    "model = Model(history_seq, pred_seq_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, None, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, None, 32)          96        \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "conv1d_8 (Conv1D)            (None, None, 32)          2080      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, None, 128)         4224      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, None, 128)         0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, None, 1)           129       \n",
      "_________________________________________________________________\n",
      "lambda_1 (Lambda)            (None, None, 1)           0         \n",
      "=================================================================\n",
      "Total params: 19,009\n",
      "Trainable params: 19,009\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our training architecture defined, we're ready to train the model! This will take some time if you're not running fancy hardware (read GPU). We'll leverage the transformer utility functions we defined earlier, and train using mean absolute error loss.\n",
    "\n",
    "Note that for this simple model, we have fewer total parameters to train than we did with the simple LSTM architecture, and the model appears to converge with significantly fewer epochs (though we are using twice as much training data). But most interesting is that our predictions end up being clearly more expressive than before, indicating that this architecture is more naturally suited for learning the series' patterns (see section 5).\n",
    "\n",
    "For better results, you could try using more data, adjusting the hyperparameters, tuning the learning rate and number of epochs, etc.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 32000 samples, validate on 8000 samples\n",
      "Epoch 1/10\n",
      "32000/32000 [==============================] - 208s 7ms/step - loss: 0.4638 - val_loss: 0.3511\n",
      "Epoch 2/10\n",
      "32000/32000 [==============================] - 224s 7ms/step - loss: 0.3225 - val_loss: 0.3050\n",
      "Epoch 3/10\n",
      "32000/32000 [==============================] - 230s 7ms/step - loss: 0.2930 - val_loss: 0.2921\n",
      "Epoch 4/10\n",
      "32000/32000 [==============================] - 219s 7ms/step - loss: 0.2841 - val_loss: 0.2877\n",
      "Epoch 5/10\n",
      "32000/32000 [==============================] - 222s 7ms/step - loss: 0.2802 - val_loss: 0.2848\n",
      "Epoch 6/10\n",
      "32000/32000 [==============================] - 219s 7ms/step - loss: 0.2775 - val_loss: 0.2840\n",
      "Epoch 7/10\n",
      "32000/32000 [==============================] - 211s 7ms/step - loss: 0.2758 - val_loss: 0.2843\n",
      "Epoch 8/10\n",
      "32000/32000 [==============================] - 211s 7ms/step - loss: 0.2745 - val_loss: 0.2836\n",
      "Epoch 9/10\n",
      "32000/32000 [==============================] - 208s 7ms/step - loss: 0.2734 - val_loss: 0.2831\n",
      "Epoch 10/10\n",
      "32000/32000 [==============================] - 207s 6ms/step - loss: 0.2730 - val_loss: 0.2830\n"
     ]
    }
   ],
   "source": [
    "first_n_samples = 40000\n",
    "batch_size = 2**11\n",
    "epochs = 10\n",
    "\n",
    "# sample of series from train_enc_start to train_enc_end  \n",
    "encoder_input_data = get_time_block_series(series_array, date_to_index, \n",
    "                                           train_enc_start, train_enc_end)[:first_n_samples]\n",
    "encoder_input_data, encode_series_mean = transform_series_encode(encoder_input_data)\n",
    "\n",
    "# sample of series from train_pred_start to train_pred_end \n",
    "decoder_target_data = get_time_block_series(series_array, date_to_index, \n",
    "                                            train_pred_start, train_pred_end)[:first_n_samples]\n",
    "decoder_target_data = transform_series_decode(decoder_target_data, encode_series_mean)\n",
    "\n",
    "# we append a lagged history of the target series to the input data, \n",
    "# so that we can train with teacher forcing\n",
    "lagged_target_history = decoder_target_data[:,:-1,:1]\n",
    "encoder_input_data = np.concatenate([encoder_input_data, lagged_target_history], axis=1)\n",
    "\n",
    "model.compile(Adam(), loss='mean_absolute_error')\n",
    "history = model.fit(encoder_input_data, decoder_target_data,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's typically a good idea to look at the convergence curve of train/validation loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10eb3eb38>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6bG2SfJSSXFhzhHxU1mdRYctiz5497N+/n2vXrvHBBx+UHqDTMXr06NqN0MEZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115007748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Mean Absolute Error Loss')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building the Model - Inference Loop\n",
    "\n",
    "Unlike in the previous notebook, we don't need to define a distinct keras model in order to actually generate predictions. Instead, we'll run our model from section 3 in a loop, using each iteration to extract the prediction for the time step one beyond our current history then append it to our history sequence. With 14 iterations, this lets us generate predictions for the full interval we've chosen. \n",
    "\n",
    "Recall that we designed our model to output predictions for 14 time steps at once in order to use teacher forcing for training. So if we start from a history sequence and want to predict the first future time step, we can run the model on the history sequence and take the last time step of the output, which corresponds to one time step beyond the history sequence.           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_sequence(input_sequence):\n",
    "\n",
    "    history_sequence = input_sequence.copy()\n",
    "    pred_sequence = np.zeros((1,pred_steps,1)) # initialize output (pred_steps time steps)  \n",
    "    \n",
    "    for i in range(pred_steps):\n",
    "        \n",
    "        # record next time step prediction (last time step of model output) \n",
    "        last_step_pred = model.predict(history_sequence)[0,-1,0]\n",
    "        pred_sequence[0,i,0] = last_step_pred\n",
    "        \n",
    "        # add the next time step prediction to the history sequence\n",
    "        history_sequence = np.concatenate([history_sequence, \n",
    "                                           last_step_pred.reshape(-1,1,1)], axis=1)\n",
    "\n",
    "    return pred_sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Generating and Plotting Predictions \n",
    "\n",
    "Now we have everything we need to generate predictions for encoder (history) /target series pairs that we didn't train on (note again we're using \"encoder\"/\"decoder\" terminology to stay consistent with notebook 1 -- here it's more like history/target). We'll pull out our set of validation encoder/target series (recall that these are shifted forward in time). Then using a plotting utility function, we can look at the tail end of the encoder series, the true target series, and the predicted target series. This gives us a feel for how our predictions are doing.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder_input_data = get_time_block_series(series_array, date_to_index, val_enc_start, val_enc_end)\n",
    "encoder_input_data, encode_series_mean = transform_series_encode(encoder_input_data)\n",
    "\n",
    "decoder_target_data = get_time_block_series(series_array, date_to_index, val_pred_start, val_pred_end)\n",
    "decoder_target_data = transform_series_decode(decoder_target_data, encode_series_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_and_plot(encoder_input_data, decoder_target_data, sample_ind, enc_tail_len=50):\n",
    "\n",
    "    encode_series = encoder_input_data[sample_ind:sample_ind+1,:,:] \n",
    "    pred_series = predict_sequence(encode_series)\n",
    "    \n",
    "    encode_series = encode_series.reshape(-1,1)\n",
    "    pred_series = pred_series.reshape(-1,1)   \n",
    "    target_series = decoder_target_data[sample_ind,:,:1].reshape(-1,1) \n",
    "    \n",
    "    encode_series_tail = np.concatenate([encode_series[-enc_tail_len:],target_series[:1]])\n",
    "    x_encode = encode_series_tail.shape[0]\n",
    "    \n",
    "    plt.figure(figsize=(10,6))   \n",
    "    \n",
    "    plt.plot(range(1,x_encode+1),encode_series_tail)\n",
    "    plt.plot(range(x_encode,x_encode+pred_steps),target_series,color='orange')\n",
    "    plt.plot(range(x_encode,x_encode+pred_steps),pred_series,color='teal',linestyle='--')\n",
    "    \n",
    "    plt.title('Encoder Series Tail of Length %d, Target Series, and Predictions' % enc_tail_len)\n",
    "    plt.legend(['Encoding Series','Target Series','Predictions'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating some plots as below, we can see that our predictions look better than in the previous notebook. They can effectively anticipate many patterns in the data (e.g. behavior across different week days) and capture some trends nicely. They are definitely more sensitive to the variability in the data than the overly conservative LSTM predictions from the previous notebook. \n",
    "\n",
    "Still, we would likely stand to gain even more from increasing the sample size for training and expanding on the network architecture/hyperparameter tuning.  \n",
    "\n",
    "**Check out the next notebook in this series** for further exploration of the WaveNet architecture, including fancier components like gated activations and skip connections. If you're interested in digging even deeper into state of the art WaveNet style architectures, I also highly recommend checking out [Sean Vasquez's model](https://github.com/sjvasquez/web-traffic-forecasting) that was designed for this data set. He implements a customized seq2seq WaveNet architecture in tensorflow.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D5/Ph7bffxvbt27F8+XK8+OKLAICzzjoLb7zxBgDgf//3f9GtWzdMnDgRmzZt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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb6d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiSpLpPBlSQBGdnW4MpDY/2VuJKUU5OXI0mSJNVhMriSJIoyV22aBgOy7kqS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\nAADgov8AT5vXUz743SYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c89278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 6007)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmILMTAPS09OwYMHH+OSTj3D+/DlUrFgRsbGxWLRoCWRZxvz5c2AyGVGhgj/G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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b07390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 33000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JRlFREXr2vAXjxj2NBQtmo6SkGIWFBVix4u94/fXXcOLEcTRo0ADp6elYuXIN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\nEgCKKEIIIYSQAPw/ib5MafEgGz4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c645e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 110005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wGGW+X7SNRiOrVr3H1auXUSqVODg4MHnyqwQHh5R4PqPRyBtvvF4hbbUlGVxJ\nAKSk5wLYZOua5uF5wdXZqykyuJIkSbIzEydOAeCXX34kOjqKF1+cWGHX+uyzT+nXb2CB4OrgwX2k\npaXy3nurAdi9ewerVr3L4sVvl3g+lUpl94EVWBFcmUwm5s2bx/nz53F0dGTRokWEhd3ex2fRokUc\nP34cNzc3AFavXo2HR9EFt6SqlXJr5MrPBsFVkzAfFOTlXfXrVs/q80mSJP1buV2YjVP8NpueUxs0\nkKyIRWV+ncFgYNmyxSQmJpCRkUGXLt145pnnWbBgDllZmaSnp7F8+X/54IP3uXjxAn5+fsTExLBi\nxUpMJhPLli1Bp9Pi6enOlCkzOHBgH6mpGubNe51Fi5ZZrhMYGMTZs2fYufN32rfvwL33PkCPHvcC\ncOzYEdatW4NKpaZ27TpMmzaTX3/9id9++wWj0cjYsc+xZMl8tm79hYsXL/D++8sB8Pb2YebMOWi1\nWubOnQnkjXJNnz6LevXqW9+pZVTu4GrHjh3odDq+/vprTp48ydKlS1mzZo3l+JkzZ1i3bh2+vr42\naahUsTQZeSNXATYIrtxdHAir5cHl2DRydQacHeUAqSRJkr2Lj79Jq1Zt6Nt3AFptLo891pdnnnke\ngA4dOjFkyHD++GMnOTk5fPzxRlJSkhk+fDAAK1eu4IknRtChQ2ciI0/y0UcfMHv2fDZsWMe8eUsK\nXCciognTps3ghx+28t57bxMUVIuJE6fSsmUr3n57CR9+uB5vb28+/HAVv/32CwBeXl4sXvw2BoPB\ncp6lSxfyxhuLqFs3jG3b/sdXX20iIqIx3t7ezJmzkCtXLpGVlVlJvVdQub/1jh07Ro8ePQBo06YN\np0+fthwzmUxER0czd+5ckpKSGDJkCEOGDLG+tVKFSUnPG7ny93YhMz3H6vM1r+dL1M0MLlxPpVUD\nf6vPJ0mS9G+UFbGoXKNMFcHLy5szZ/7h2LEjuLm5o9frLcfq1g0HICrqKi1atATA19ePOnXqAnD5\n8mU2bPiEjRs/Ra1WolQWvSDq4sUL1KtXnwUL3kQIweHDB5k79zU++eQLUlJSmD17OgBabS4ODg4E\nBgZZrp/ftWtRLFu2GMgbdQsPr8fo0c8SGxvDjBlTcXBwYNSoZ23RNWVW7uAqMzMTd/fb1blVKhUG\ngwG1Wk12djZPPfUUY8aMwWg08vTTT9OiRQuaNGlS5Pl8fFxRq1XlbY5FcXv9SEVLy9bj5uKAi5Ma\nFxv0YdcNFilfAAAgAElEQVQ2ofx8MJor8Zk80LlmTQ3Kz6D1ZB9aR/af9WpCH3p4OOPq6mi5159+\n+pbQ0FpMmTKFK1eu8OOPWwkI8MDJSY2PjxsBAR60bduSX3/9lYAADzQaDTduxODn506jRg146aWX\naNWqFRcvXuTEiRMEBHigVqvw83PD0dHRct1t245z5coVFi9ejFKppH37Vri5uREREUatWkF88snH\nuLu7s2PHDjw9PYmOjsbNzYmAAA8MBgNKpYKAAA/q16/P+++/S61atThy5AgajYYrV87SsGE4Eye+\nyNGjR/nggw9Yv359pfdtuYMrd3d3srKyLP82mUyWXaxdXFx4+umncXHJm2Lq3LkzkZGRxQZXGk12\neZtiYc+bbcYmZrLxt/M8fn9Du0zyTtRk4+flnPd3G/Shv5sjjmolx87GM7BruNXnqy7s+TNYXcg+\ntI7sP+vVlD7MyMglO1tnudcmTVozf/4s9u8/gLOzC8HBoZw/H4VWayA1NZvExAxaterI9u07GTJk\nKL6+fjg6OpKWlsvzz09i2bKl6HQ6hDAyYcJUEhMzaNGiNaNHj+X992+nDfXtO4RVq96lb99+uLq6\nolKpef31eaSkZPPiiy8zevRYhBC4ubkzZ84CTp8+b2mnwWDAZBIkJmYwefJ0Xn55CkajEaVSycyZ\nc3Fzc2flyg9Yt+5TFAoFY8c+V2HvZXEBuEIIIcpz0t9++43du3ezdOlSTp48yapVq1i3bh2QNzw4\nZcoUtm7dislkYuTIkSxcuJBGjRoVeT5b3Lw9/4dYseUkp6+k4OfpxPyxHYutcl7ZcrQGxr+7h5b1\n/VgyvrvN+nDF1yc5fTWFd8Z3w8fDySbntHf2/BmsLmQfWkf2n/VkHxbt6tUrXLlymQce6I1Go2HU\nqOF8993PlsEVqDn9V1xwVe6Rq969e7N//36GDx+OEIIlS5awfv166tatywMPPEC/fv0YNmwYDg4O\nDBgwoNjA6t/uwvVUTl9JwcVJRXK6lo3/d54XBjS3m42NzWUYfD1tGwA1C/fl9NUUzkWn0LWFdbWz\nbCEjW0d6tp5Qf7eqbookSVK1FBRUizVrVvL115swmUyMH/9ygcBKylPuHlEqlSxYsKDAYw0aNLD8\nfdy4cYwbN678LfuXEELw3Z4rALw8pDXf/nmZI5EJtKjvS49WJRdMqwzmMgy2Hl1qXs8XdsOZq5oq\nD67Ss3TM33CE9CwdS57rbJNVkZIkSTWNq6sry5a9W9XNsHtyb5IKdiYqhQvXU2ndwI+IOt48168Z\nLk5qvvz9IjdTrM8zswVzdXZfD2ebnjc0wA1PVwfORqdQztlnmzAYTazedhpNhhajSfDr4WtV1hZJ\nkiTp308GVxVICMF3f+aNWg3qmVfEzN/LhacfboxWb+SjH85gMJqqsolAxU0LKhUKmoX7kpap40ZS\nVskvqCBf77rEheup3BMRQKC3C/tO3bAElJIkSZJkazK4qkAnLiYRdTODDk0CqRt0O/GtU7MgurWs\nRfTNDMuUYVUy17jy9bTtyBXk5V1BXrX2qrDvVBw7j8UQ6u/GM32a8miXMAxGwW9/ydErSZIkqWLI\n4KqCmEyCrXuvoFDAwB5313ka0TuCIB8X/u/wNc5EpVRBC28zV2eviBV9zcJ9ADhbBfd4NS6dz347\nj6uTmgmPtcTFSU3XFrXw8XDijxOxpGfrKr1NkiRJ0r+fDK4qyF/n4olNzKJri1oE+929Os3ZUc1z\n/ZujUipY9+PZKv2iT8nQ4uasxsnB+iKud/L1dCbYz5Xz11IrdQo0LUvHqu/+wWg08fyA5gT5uAKg\nVil5tHMYOoOJ349cr7T2/BsYjCZ+2HeVeDvJFZQkqfyOHz9K3769mTDhOSZOfJ7nnhvNt99+Vebz\nrFmzkl9++ZGLF8+zfv3HRT7vzz93k5SUSHJyEsuXL7Wm6dWCDK4qgMFoYtveq6iUCgYUs3FxvWBP\nBveqT1qWjvU/n6uSpG8hBCnp2gqZEjRrFu6LVm/kcmxahV0jP4PRxJqt/6DJ0DK4V31a1vcrcLxH\nq2A83RzZeSyGrFx9EWeR7nTqcjLb9l3lh/1Xq7opkiTZQLt27Vm1ai0rV37EqlVr+eqrTWRklK8+\nVaNGjRkzpugKAd98s5msrCz8/PyZNm1GeZtcbcjiFKWk1RvJzjWUaups/z9xJKTmcN89ofiXsOT/\n4Y51OX0lhb8vJ7PreCwPtKttqyaXSrbWgFZvxLcCi3w2D/dl57EYzkRpaFzXp8KuY/bVzotciEmj\nfZNAHu0cdtdxRwcVD3eswze7L7PzWAz9iwmApdvO3cqbOxutQQhhN3XaJKm6a/f5ukIff6lNe55p\n2Sbv7zt+5XBc7N2vDQpm7UN9APj87CneO/YXx0aWfT+97OxslEolkye/RHBwCBkZGbz99nu8885S\nYmKuYzKZGDfuRe65pz1//LGTjRs/wdvbB71eT1hYOMePH+X77//H/Plv8s033/D555swmYx0796L\npk2bc+nSBRYtmsucOQtZtOgN1q7dwJEjh1i7dg1OTk54enoxc+ZcLl48z6ZNn+HgoCYu7gb339+b\nUaOe4c8/d/HFFxtRq9UEB4cwe/Z8lEr7HR+SwVUhsnP1XIvPJDo+g2vxGVyLz+RGchZCQLuIAIY/\n0MiyVcyd9AYjP+yPwkGtpG+X8BKvpVQoeLZvM9749C++3nWJJnW9CQ1wL/F1tqK5lczuU4EjV43r\neqNUKDgblcLgW6smK8reUzfYdTyW2gFujH20SZEBwL1tQvnlYDS/H7nOQx3q4Owo/yuU5Gx0Xt5c\nWqaOG8nZshirJFVzx44dZcKE51AqlajVaqZMeZVNmz6jd+9H6NXrPrZu/RYvL29mzpxLWloq48c/\nxxdfbGH16v/y8ccb8fT04tVXXy5wTo0mhY8//phPP92Eg4Mjq1a9S5s299CwYQSvvvo6Dg55u5MI\nIVi2bAmrV68jICCQLVs2s3HjJ3Tt2p34+Dg2bNiMXq9n4MBHGDXqGX7//Tcef/xJHnzwYX799Sey\nsrLw8LDf/R/lN8otN1Oy+W7PFaJvppOYmlvgmJOjioahXhiMJo5dSOSfK8n06RrOIx3r4qAuGDn/\ncSJvmf8jHeuWOkHcx8OJUY805oOtp/ntr+uM7dPUZvdVkpRbyewVOXLl4qSmfqgnl2PTyMrV41ZB\nW/9cuZHO57+dx81ZzYTBLYsNmFyc1PRuX4dt+67yx4kbPNKpboW06d9Ck6ElLjkblVKB0SQ4G5Ui\ngytJspHSjDStfvA/JT5nZLNWjGzWqtTXbdeuPfPnv1ngsU2bPqNu3bwR/8uXL3Hq1AnOnj0NgNFo\nICUlGTc3N7y8vAFo0aLg9WJjY2nUqBFOTnm/sE+a9Eqh105NTcXV1Y2AgEAA2rRpy0cfraZr1+7U\nr98QtVqNWq22nGfixCl8/vkGtm37H2Fh4fTseW+p77Mq2O+YWiX782QsRyMTyNEaaR7uw3861eWF\nAc1Z8lxnPpjSk5lPtWP20+15tm9TnJ3UbN1zhTmfHOafK8mWc2h1Rn4+GIWzo4r/dC7bl3XbiAD8\nvZw5EplArs5g47sr2u0yDBW791/zcF+EgMjo1Aq7xje7L2E0Cp4f0JzAWwnsxXmgfW2cHVX89tc1\ndHpjhbXr3+DcrVGr+9qG5v27ikprSJJU8czTbWFh4Tz44MOsWrWWd975L/fd9yAeHp5kZmah0eT9\nDIiMPFvgtaGhtbly5Qo6Xd4irdmzp5OYmIBSqcRkur2oydvbm+zsLJKSkgA4efI4derkfW8WNuHw\nww9beeaZ51i1ai1CCPbs+cPWt21TcuTqlqS0vBGchc90xMu98EBDoVDQtUUwbRoG8P2+q+w8FsO7\nW/6mbSN/nnigEYfPxZOerad/t3A8XB3LdH2lQkH3lsFs23eVI5EJlbY1zu2tbypuWhDygqvv913l\nbFQK7RoHVMg1bmqy8fNypkU9v5KfDLg5O3D/PbX55VA0e0/FVXq+W3Vy9lYw1b1VMP9cSSbymgaj\nyYTKjnMeJEmyzoABg3nrrUVMmPAcWVmZDBo0FAcHB15/fS6vvDIBDw+vu/YV9PHxYdy4cUyY8BwK\nhYJu3XoQEBBIixatWLToDaZPnwXkfZ9Onz6LWbNeRalU4OHhyeuvz+PKlUuFtqVp0+ZMnjweLy8v\nXF1d6dq1e4XfvzUUoir3JcnHFjtoW7MT9/wNR7iRlMWHr/QqdaJuTEImX2w/z4WYNBzUSpRKBWql\ngrde6Iqrc9nj1qS0HF5bc5CGtb2Y+VS7Mr++PD756Sz7T9/kzec7E+TjWmG7mRtNJia+txcfDycW\nj+ts8/MbjCaef/sPGtXxZsaIe0r9uvQsHdPXHMDd1YGlz3dBrbIuWPg37gYvhGDa6gPoDSbem9Sd\nTdsvsPtELK+PbEfDUC+bX+/f2IeVSfaf9WQfWqem9F9AQNE5X/LXzluS03Lx9XQu0wqo2oHuvDbi\nHsb1a4arkxqtzsh/OoeVK7CCvK1xmob7cDEmrdL2HUyx7CtYsdOCKqWSsCAPbiZnk6O1/bRnaqYW\nQdmnNz3dHOnVJpSUdC0HT9+0ebv+DW6mZKPJ0NI0zAelQkHTsKorDCtJklQdyOCKvFypzBw9/uXI\nO1IoFHRpXoslz3Xm5SGteKSjdYnR3VsFA3nlHCpDSnouHq4OOKhtX0D0TuHBHgjgekKmzc9tzh0r\nT5X5RzrVRa1S8POhaIymqt/r0d6YpwTN1fabhPmgyPe4JEmSVJAMroCkWxsX+3kVX5OqOC5Oalo3\n9EeptK72zz2NAnBxUrP/nzhMpoqdsRVCoMnQVsi2N4UJq5U3hBp10/bDxbdXPZY9d8zHw4nuLYNJ\n0ORwJDLB1k2r9swjVE1v7RPp7uJAWC0PLsemodXJhQCSJEl3ksEVeVOCQJG1qyqTo4OKzs2CSM3U\ncfpqxU67ZOUa0BlM5QpIyiO8licA0TfTbX5ujZWrHv/TOQylQsEP+6JISM2xZdOqNZNJEHktFX8v\nZwLzFcRtFu6L0SS4EFNxqz8lSZKqKxlcAclpeV+m/nYQXMHtqcF9p25U6HVSbo3YVXQZBrNAHxec\nHVUVNHJlzh0r33sY4O1Cr7Yh3EzJZtbaQ3y+/TypmVpbNrFairqZQY7WYJkSNGtahRtyS5Ik2TsZ\nXJFvWrACq5SXRXgtD0ID3DhxMYmMCtzQ+XYZhsoJrpQKhSWp3da1vMyBoo8VgeKI3hE83785fl7O\n7D4ey4wPD/LNH5dq9P6D5vpWzW5NCZo1CvVCrVLKvCtJkqRCyOCK29OC9jJypbhV88poEhw6G19h\n19FYRq4q777DauUltV+Lt21Se0qGFge1Eg+X8ld/VyoUdGoWxKJnOzHqkca4uTjw66FrTF9zkJ8O\nRFVqcdfyiE3MZN6nf3HRhlN15uCpSVjBkStHBxWNantxPSGT9Ar8BUCSJKk6ksEVecGVSqnAu4ji\noVWhS/NaqJQK9p+quFWDlVWGIb/w4IpJatek5+Lj4WSTzYTVKiW92oTy5nOdGXZfQ1RKBd/tucKM\nDw+y81hMhS80KK+Tl5K4lpDJxz+etUkgqNMbuRiTRp1AdzwLKYprniqMjJajV5IkSfnJ4Iq8aUEf\nDyerV/rZkqebI60b+nMtIZPoCshRgvw5V5U3clURSe16g4n0bL3Ng0RHBxWPdKrLWy90oX+3cLQG\nE5t+v8CSL45xIynLpteyhdjEvDYlpeXyvz+vWH2+S7FpGIymu/KtzMxThTLvSpIkqaAaH1zpDUbS\nMnV2MyWYX/eWtxLbK6jmlebWyFVljthVRFK7JrNit/BxcVIzsEd93nq+C52aBXHlRjrz1h/h54NR\ndlUXKzYpC0e1kmA/V3Yei+HCdeumB81Tgk3DfAs9HhbkgauTWuZdSZIk3aHGB1fm4pP2UIbhTi0b\n+OLl5sihMzfRG2xfTyglXYunmyMO6sr7GFREUrumklY9ero58nz/5kwc3BI3ZzX/+/MKiz47RkwF\nFEUtK6PJRFxyNsH+box5tCkKYP0v59BasSH1uegUVEoFEXUK3+JGqcyr1p6UlivLV0iSJOVT44Mr\n84bN9rJSMD+VUkmXFrXIyjVw4mKSTc8thCAlQ1up+VZmtk5qT7HUuKqc97BtRAALn+1E1xa1iL6Z\nwfwNR/hh/1UMxqobxUrQ5GAwmqjt70bDUC96d6hDvCaHbXvLNz2YlasnKi6DBiGeODsWvZ1TM1mS\nQZIk6S41PrhKTjevFCx/dfaKVFFTgxk5egxGU6XmW5mF27hS++3q7JUXKLq7OPBs32a8PKQVnm6O\nbNt7lUUbj3IlNq3S2pCfOQcsJMANgEE96xPo48L2I9e5XI42RUanIri7BMOdmlryruTUoCRJklmN\nD66S7Kg6e2FC/N1oEOrJmSsplgR0W9BYsReftczb4Ngqqd2y6rEKAsXWDf1Z+ExHerQK5lpCJq+u\n3GvJZatM5mT2UP+84MrJQcXYR5uCgE9/OVfmaeWz0eYtbwpPZjcL8nHB19OJc1EpmIR9rqKUJEmq\nbDU+uDJXZ7fX4AryRq8EcOD0TZuds7Krs+cX5Otq06T2qgwUAVydHRjzaFP6dwu/Vb6g8reEiU0y\nB1fulsci6nhzf7vaxCVn8/2+qDKd71yUBidHFfWCPYt9nkKhoFmYL1m5Bq7buHaZJElSdSWDq7Rc\nFIrKnVIqq45Ng3BUK9n3TxzCRqMD1m4XYw1bJ7WnpOfi6KDEzbno3KDK0LiON2D7AqmlcSMpC2dH\n1V3B8pBeDfD3cub/Dl/jalzpRgpT0nO5mZJN4zreqFUl/4iw5F1Fy7wrSZIkkMEVyem5eLs7lepL\npKq4OKlp1ziQBE0OF2Nsk9NjzlOqqtEeWya15yXmO9ukgKg16gTlTXdeS6iYumRFMRhN3EzJJtTf\n7a4+cHJUMebRppiEuDU9WHLS/blbRUFLyrcyaxpmTmqXeVeSJElQw4Mrg9FESobWLmtc3alri1oA\n/H3JNqsGNZYVdlUTXIVb8q6sC0R0eiOZOfoqu4/83F0cCPRxqfSRq/iUbIwmQcitfKs7NQ3z4d62\nocQmZvHTgagSz2de+dcsrPh8KzMvdydCA9y4eD21QkqGSJIkVTc1OrhKzdAihH3nW5mZk8BjbVQZ\nPCU9FwWVW0A0vzDLikHrkto1lbz5dEnqhXiRnqUjNbPyktpv51sVHlwBDL23AX6eTvxyKJpj5xOK\nnF4WQnA2WoOnqwOhAUWf707NwnzRGUxcirVd5X1JkqTqqkYHV/Zc4+pO7i4OeLo52mzblZQMLV7u\njlU2HWqrpHZLYn4V5I4VpkFoXsHNa/GVNzVoWSkY4F7kc1yc1Ix5tCkAH2w9zTtfnyQm8e4RthvJ\n2aRl6mga7lumaVZz3tU5mXclSZJUs4Or2zWu7OOLuSQhfq4kp+Wi1Vk39WISAk2GtsK2iykNWyW1\n3y7DYB8jV/VvBVfRlTg1aKlxVczIFeTlUM0f25EW9Xw5G6Vh3qdH+GL7eTJz9JbnnCvjlKBZRB1v\nVEqFzLuSJEmipgdXdl7j6k4h/m4I4GZKtlXnycjSYTSJKg9IbJHUXpU1rgpTP9S8YrDyRq5ikrJw\nc1bj7e5Y4nND/N2YMqw1Lw9pRYCPC7uOxzLzo4P8fvQ6BqPJksxeUn2rO7k4qakX4snVuHSyc/Ul\nv0CSJOlfrEYHV9VpWhBuj0xYOzVYlWUY8rNFUrt5X0F7ybny93bG3cWh0oIrvcFIgiabkEJWChZF\noVBYip8Ov78hJgGbd1zkjU//4ly0hkAfl3LtWNAszAchIPJa5df5kiRJsic1OrgyTwtWl+DKnLBs\nbVJ7ShUX3TQLs8E2OPYSKJopFArqBrmTmJpLdq5tNqYuTlxyNkIUn8xeFLVKyUMd6/Lm8525t00I\nN1OyydUZyzwlaGYu3RB5TU4NSpJUs9Xo4CopLQdPN0ccHVRV3ZRSCbbZyFXVVWfP73ZSe/lXmKWk\n5+LsqMK1iguI5lf3Vr2r65VQ78qyUrCYZPaSeLo68vQjTZg3piMPtqvNfzqHles8Ybfu25xgL0mS\nVFPV2ODKJAQp6dWjxpWZp6sj7i4O3Ei27svrdo2rqr13pUJBXSuT2jUZ2iq/jzvVDcoLdCojqb20\nyeylUSfQnSd7RxDgXb5NzJ0cVfh5Olv9+ZQkSaruamxwlZaZl9RdXaYEzUL83UhMzUGnL/+KQcvI\nlR3kKYVbkdSu1RnJyjXYxX3kZx7BuV4JeVe3yzBYH1zZQoi/G2mZOrJkUrskSTVYjQ2ukqrBhs2F\nCfF3QwjrVgymZGhRKMCrFKvLKpo1Se1VvYVPUYJ8XHF0UFbKyFVsUiYerg54ulb9ewkQ4u8KQFyS\ndStaJUmSqrMaG1yZyzBUp2lByKt1BVg19aK5tZ+iSln1b781Se0pdjK9eSelUkGdAHfikrNKtZdf\neWl1RhJTc8uVzF5Rgv1u5QXKqUFJkmqwqv92rSLVbaWgWaiVSe0mkyA1U1flyexm5qT26HJMod2u\nzm4f95Jf3SAPjCZBbFLFjV6ZA5hQ//Ins9uarcqFSJIkVWc1NrhKqq4jV5Yvr/JNu6SZC4jaSekC\nc1J7XHJWmZPaNXZWQDQ/c1J7RW7ibElmt5N8K7DNyKokSVJ1V2ODq+pWnd3M080RN2d1uUcG7DFP\nKbyWB0KUPRCxx3sxM5djKM+IXGlZktntaFrQ1dkBb3dH4uTIlSRJNViNDa6S0nJxc1bj7Gg/9ZFK\nQ6FQEOzvRoImp1z5PPZShiG/sHImtd/OubK/4Kp2gBtKhaJCK7XfrnFlP8EV5OVdJadrrdozUpIk\nqTqrkcGVEIKU9NxybfFhD0L83DAJQbym7FODtyua209AEl7OpPaUDC2uTvYZIDuoVYT4u3I9IROT\nSVTINWKTMvFyd8TN2aFCzl9e5qnruGS5YlCSpJqpRgZXGdl6dAZTtZsSNLMmadicBO5jR6M95U1q\n12Tk2uWolVndIA90elO5guCS5GgNpKRrqW1HU4JmMqldkqSarkYGV9Vtw+Y7WbNi0N724oOCSe1a\nXemKo+ZoDeRojfjY0X3cqSLzrm5XZreflYJmMqldkqSarkYGV+YyDNVtpaCZNSMDmoxcVEoFXm72\nUXTSzJLUXsr9+CxlGOx55CowL/C5XgErBu013wryTQvKQqKSJNVQNTK4qq7V2c283R1xcVJxoxw5\nLSnpWrzdnVAqFRXQsvKzFBONK2VwZYe5Y3e6XY7B9iNX9rhS0MzDRntgSpIkVVc1MriqrtXZzRQK\nBSF+bsSnZGMwln7FoNFkIjVTa1f5VmZlTWq35xpXZq7ODvh7ORMdn4kQtk1qNxcntcWGzRXBFntg\nSpIkVVc1OriqriNXAMH+bhhNgnhNTqlfk5apQwj7HO0J8nXFyVFF1M30Uj3fkphvh/eSX1iQB5k5\nekswaCuxSVn4eTrh4mR/KyXBNntgSpIkVVc1MrhKSs/F2VGFq51+MZVGiJ85r6X0Uy8pdjzao1Qo\niKjtTVxydqkCEXvdV/BOFVGpPTNHT1qmzi6T2c3MSe2yHIMkSTVRjQuuhBAkp+Xi7+WMQmFfeUdl\nYU5kLktSuz3vxQfQPNwHgDNXU0p8rj1XZ8/PvGLQlnlX5vfcHvOtzIJlOQZJkmqwGhdcZeUayNUZ\nq20ZBjPzyFVZkobNoz32Wr6geT1fAM5ElSK4Stfi7uKAk4OqoptllYoox2DPKwXNyvP5lCRJ+reo\nccHV7WT26lmd3czX0wknR1WZRgZuJ4Hb52hPiL8b3u6OnI1KwVRMArgQAk2G1m5H4PLzdnfE09XB\nptOCsYn2ncwO5hWt5d8DU5IkqTqrecFVevVPZgfzikFXbqZkYzSVbsWgeSrNXvOUFAoFzev5kpGt\nL7Y2VLbWgFZvtPspQci7pzpBHiSn55KZo7fJOW8kZaHg9uiQPVIoFIT4u5KgySnTitaqFpOYWaH7\nQUqSVDPUuOAq6V+wUtAsxM8Ng1GQUMoVgynpWlRKBR6u9rUXXX6lmRqsLsnsZuak9us2+tKOTcrC\n39sZJ0f7nhIN9stb0Vraz2dVy8zR89am47zz9cliR04lSZJKUu7gymQyMXfuXB5//HFGjhxJdHR0\ngeNbtmxh8ODBDBs2jN27d1vdUFup7jWu8rtdqb10K7JSMnLx8XBCaceJ/M3CbwVXxSS1V4fq7PmF\nmZPaE6yfGkzP0pGRrSfUjlcKmlnyrqrJ1OAP+66SlWsgI1tfplW4kiRJdyp3cLVjxw50Oh1ff/01\nr7zyCkuXLrUcS0xM5PPPP+err77ik08+YcWKFeh0Ops02FqWacFqMupRHEtwVYqkYYPRRHqmzu5H\nezxdHQkL8uBiTGqR+wxq7HB/xOLYcsVgdUhmNyvL57O0rsVnWHZYsKW45Cx2n4jF/GvHxZg0m19D\nkqSao9zB1bFjx+jRowcAbdq04fTp05Zjp06dom3btjg6OuLh4UHdunWJjIy0vrU2kJSWg6NaaddT\nY6V1ew+3kr+8ElNzEFSP0Z7m9XwxGAXnr6cWery6lGEwC/RxwclRZZOk9uqQzG4W4n9rA2cbjQLF\na7JZuPEocz75ixMXEm1yTrMtuy5hNAkG9awPwMWYwj97kiRJpVHuKpqZmZm4u9+emlCpVBgMBtRq\nNZmZmXh4eFiOubm5kZlZ/BeLj48rarX1OSQBAR7FHtdkaAn0dSUw0NPqa1U1Pz93HB1UJKTmlnjf\n2w5EAdC5ZUiJzy3peEXr1jaUXw5FcyU+gwc6h991PFuXlyDdMNyPADsMMgrrv/ohXpy/psHT29Wq\n8hEpWXlJ8S0jAqv8fSqJn587To4qEtNK/nzeqbDnf7b9AkaTQBhMrPzuH576TxOGPRBhdb26kxcS\n+PtyMi0a+DGqXwt+PxrD5bgMu+/f4lTnttsL2YfWqen9V+7gyt3dnays27+Rmkwm1Gp1oceysrIK\nBCrI2DkAACAASURBVFuF0Wisr+QcEOBBYmLRUy852rx8irCg4p9XnQT7unI9IYP4+PQiN2POzNHz\n28FofDycaFbHq9h7L6kPK4O/myOODkqOno1nYNfwu47fSMhrn9Abqrytdyqq/0J8XTkXlcLJszep\nH1L+wP7ydQ0KBTgphN3de2Fq+bpyPT6z2M/nnQrrw5jETP48HkPdQHfGPNqUld+d4otfI7kQlcKY\nR5uWO2A1mQQffXcKBfBYj/okJ2fSIMSTk5eSuHAlqdqMjuZnD/+HqzvZh9apKf1XXABZ7mnBe+65\nhz179gBw8uRJIiIiLMdatWrFsWPH0Gq1ZGRkcPny5QLHq4o53+rfkMxuFuLvit5gIrGYPJTdx2PQ\n6o081KEOapX9LxB1UCtpUteHG0lZluT1/FIytHi6OuCgtv97Mbu9DU75f+AIIYhNzCLQxxUHG4zy\nVoa8Fa3Ffz5LY+ueKwhgcK/6hNXyYM6oDjSs7cVf5xJY+sXxQj8npbH31A1iErPo1jKYsFubhzeq\n7QXIqUFJksqv3N9OvXv3xtHRkeHDh/Pmm28yc+ZM1q9fz86dOwkICGDkyJE8+eSTjBo1iilTpuDk\nVPW/Af4bNmy+U0gJ24zo9EZ2HIvB1UlNz9Yhldk0q1hWDd5RksFcQNReq8wXxRZJ7amZOrK1Bmrb\n4VRoUWyRd3U1Lp0TF5NoGOpFy/p+AHi5OfLq8Lb0aBVMdHwGCzYe5VIZk9BztAa27rmCk4PKkmsF\n0Ki2NyCT2iVJKr9yTwsqlUoWLFhQ4LEGDRpY/j5s2DCGDRtW/pZVAEuNKztfMVcW+YOrto0C7jq+\n/584MrL19OkShks12qjaUu/qago9Wt0OCjNz9OgNpmqRmJ9fiL8bKqWCaCuS2mOTqk8yu5llg/Hk\nbNo2Kt85vvvzMgCDe9YvkF/loFYy+j9NqBPozlc7L7Fs83FGPty4wOelOD8fjCY9W8/AHvUKTP+F\n1fJArVLKkStJksqt+syr2MDtacHqvfVNfsXVujKaTPzfX9dQq5Q82L5OZTfNKiF+rvh4OHE2SlOg\noKOlgGg1G7lyUCsJ8XcjJjGz1BX173QjsfqUYTAraWS1JJHRGs5EaWge7kOTMJ+7jisUCh5sX4cp\nj7fGyUHF+l8i2fBrJBnZxZd+SUrNYfuR6/h4OPFwx7oFjjmoldQP9uB6QiY5WkO52i1JUs1Wo4Kr\nf1N1drMALxfUKmWhtYSOnU8kMTWX7i1r4eXmWAWtKz+FQkHzcF8yc/QFptJub+FTvUauIC/vSm8w\ncTO5fIs3LDWuqtHIlb+3c97nsxzBlRCC7/ZcAWBQzwbFPrd5uC+zR7Un1N+NPX/fYOZHh9h+5HqR\nW+98++dlDEYTQ+5tUGgyfKM63ggBl2/IqUFJksquRgVXyWm5qJQKvNyrV6BRHKVSQbCfK3HJWQVG\neIQQ/HroGgq46zfz6iL/1KCZuYBodVzFVScwL+8qJrF8ozixSVmolAqCfF1t2awKpVIqqeXrSlxy\ndpm3lPnnSjKXYtNo28i/VCssg3xceWNMB4Y/kDf/+NXOi8z55C/+vpSEyHftSzFp/HUugXrBnnRq\nFlTouSxJ7ddlcCVJUtnVrOAqPRc/T2e73v6lPEL83dDpTZaEfYBz0Rqi4zP4//buOzCu6k4f/nPv\n9NFImhlp1K3mJveGGzaGgKkBAiw2mAVCSTZkYQk9IckmJDgQspv8NiSbfVlIWOKEGkoqHYMxrti4\nyLbcJFmW1fuoTbn3vn+MZlSsNk0zd/R8/sKamXuPrmX8+Jzv+Z4lMx2q+st4oNmFNggYHK7Udq7g\nQHl9y3nVjcHXXSmKgjNNXciym1Wx43OgnHQzXB4JrX2/d+MhKwre+KQcAoBrzyse8/1+Wo2IS5ZO\nwZPfWIELF+eisbUHv/zTAfzi1f0409gJWVHw0ofHAQA3XjRtxP8XTM1NhQDuGCSi0KinwjlMbo+E\nji43coep21C7nLT+HVkOq6+e7O0dvrMeL19RELNxhSvZrEd+VjKOV7fD5ZZg0Gv6lwVVOHOV5/C1\nYzgTwsxVc0cvXG5JVfVWfoEzBpu7xr0kv+doI6oaOrFidibyMoI/RzHZrMfNl8zElxbl4uWPTuBQ\nRQt+8LtdmF1gQ0VtB5aWZAR2BQ4nyahDriMJ5TUd8Eqy6gItEcXWpPk/RuBMwQSqt/LL6TvE1193\ndarOiUOVrSjJt6IoW92d6OcW2SHJCo6ebgXgm7kSAFhVGK5SkvRIMetCmrny1yypaaegX7BF7ZIk\n480t5RAFAV85ryise+c6LHhg/QJ86/r5yLCZcaiyFVqNiHUXjF7DBfhaMri9ckSOLSKiyWXShat0\nFS4njWVoL6F3dlUBUPesld+cvn5XpX1Lg63OXqRY9KqdSch1WNDU3hv0LjT/bFduevCzOLGWHWS4\n2rynGnUt3Vg9PxuZtvCXtAVBwIJp6Xj8zmW47fIS3H3tXKRbx94xPI3NRIkoROr8GyoEibhT0C/D\nZoJGFFDT1IXGth7sOlKPPIcFc/sKwtVsam4qDDoNDlW0QO5rIKrGJUG/wNJgkLvnAjsFVbgsmGkz\nQRQE1I5jl6THK+Ol98qg1Qi4elVhRMeh1YhYsyAHC6alj+v9/Z3aWdRORMGZNOHKXwidSEff+GlE\nEVlpZtQ0dePdXVVQFODyFflhH2gbD3RaETPzraht7kZVvRNeSVFdj6uBQi1qP9PYBa1GRMY4Zlzi\njVYjItNuQk1T16Bde8PZsr8GDa09+NKivJhvWkhLMcKWbMDx6rYxx01ENNCkCVdzi+w4Z6ZD9TVI\nI8lJS4LLI+GTfTVISzFiaUlGrIcUMf6WDJ/urwUA2FTY48rPX5x9pmH8M1eyrKCmuQs5aeZxH34c\nb3LSktDt8qK9a+Tmnu2dLvx1WyWMeg2+vDL2S9qCIGB6Xiqc3R7Ut4Z3NiIRTS6TJlzNmGLFv147\nD/phGgYmAn9jSUlWcMkydRzQPF7+uqsdh+sBqK87+0A5aUkQ0H+UzXg0tvfA45VVuSToN1bdVa/b\ni/967QA6uty48eKZSImTprf95wyy7oqIxi9x/gae5Pw7spKMWqwZ59lqapHddxSOvwhcjd3Z/Qx6\nDRxWE6obx14i8/MXs6txp6Cfv13IcHVXXknGb94qxal6J86bn43rvjRtooc3ItZdEVEoGK4SxNTc\nVBj1Glx1biEM+sSanRMEIbA0CKh75grwFaV39nhGXSIbqL+YXX07Bf1GasegKAp+/+5RlJa3YF5x\nGm65dGZc1QrmOSwwGTQMV0QUFIarBGFLNuDX96/BJSo96mYsA3c+qnnmCujfMTjeovYzfe9T05mC\nQ2XZzRBwdrj689YKbD1Qi4KsZHzzmjlxt5wtigKm5qaivqUbHeMMw0RE8fV/MgpLoh3rM9CsAt9R\nOIIA1Z8N6S9qrx5nUXtNUxcMOo2q24jodb7l0IEHjG/ZX4O/fFaJ9FQj7lu3AEZ9fB4Y0V93Ff3Z\nq+b2Xuw73hT1+xBRdDFckSokm/WYPzUN03JToRHV/WPrb8dwZhwzV15JRm1zN3LSk1QfnrPTzHB2\ne+DsduPAySb8/p2jsJh0eOCGhUiNkwL24UzPnbhmoq9sPoGnXz+A2ubQDvcmovgQn/9UJBrGv10/\nH+qOFz4ZNhO0GhHV4zhjsKG1B5KsqHpJ0C8nPQn7TzZje2kd3vi0HBqNgHuvn4+sOD9YvCgnBRpR\nmJCZq4oa3z3KTrUiO039v+dEk5W6pwBoUhEFIa6KnUOlEUXkpJtR09wFWR59x6CaO7MP5S9qf/mj\nE/B4ZHzj6jmY1jcrFM8MOg0KspJRVe+Eyy1F7T7Objea+5odHz3N1g9EasZwRRQDeQ4LPF4ZDW2j\nN6dMhGJ2v4GtJG66eAYWz3DEcDTBmZ6XCklWUF7bEbV7nKp3Bv77aBW7whOpGcMVUQz4Z6KqG0av\nu0qENgx+eY4kTMtNxbVrinHRkrxYDycoE9FM9FSdL1wlGbVo73KzKzyRijFcEcXAeNsx1DR1wWTQ\nwqryHZIAoNNq8N1bluCqcwtjPZSgTZuAZqKVfeHqgkW5AICjVa1Ru1esKIqCfceb4PJEb3mVKB4w\nXBHFgD9cnRmlqN3jlVHf0oNcR1JC1JqpWYpZjyy7GSfOtEOS5ajc41SdE8lmHVbMyQKQmHVXe442\n4unXD+Avn1XEeihEUcVwRRQDVoseSUbtqDNXdS3dkJXE2CmYCKbnpcLllsbdnywYnT0eNLX3oiAr\nGTlpZiSbdQlZd7WrrAEAsPNwPeQE+96IBmK4IooBQRCQ67CgobVnxCWSRCpmTwTRrLvy11sVZCZD\nEATMmGJFq9OFxvbeiN8rVlweCQdO+hqktnS4cIJHClECY7giipE8RxIUnH0kjF+gmJ3hKi5Mn+Kr\nuzpxJvKhoLLOtwuxMCsZADBzii/IJVLd1cGTzXB7ZEzNTQEA7DhcH+MREUUPwxVRjIxV1O6vx0qE\nnYKJIMNqQopZF5Wi9sDMlT9c5dsAAMeqEqfuanffkuDNF89ESpIen5c1wCtFp36NKNYYrohiZKyi\n9pqmLlhMOqTE8dEwk4kgCMjPSkar04XuXk9Er11Z54TFpENaiu/8yFxHEpKMWpQlSLhyeSTsP9mE\nTJsJ+ZkWLCvJQGePB4crW2I9NKKoYLgiipHcUc4YdHkkNLb1BM4hpPjgP6qnriVyPagGFrP7d4WK\nfXVXzR29aGpXf7+r0nLfkuA5JRkQBAHLZ2cC4NIgJS6GK6IYMRm0SEsxDHvGYG1zFxQM7mpOsdcf\nriK3Y9Dfmd1fb+XnXxo8mgCzV/4lwXNmZgAAinNSkJ5qxBfHmqJ6pBBRrDBcEcVQrsOC9i43nN3u\nQV9nvVV8isbM1cCdggMFitpV3u/K7ZGw/0QzMqy+JUHAt8S6Yk4mXB4J+040xXiERJHHcEUUQ/1F\n7YNnQrhTMD71h6vuiF3T35l96MzVlAwLTAat6ovaD5a3wOWRAkuCfstn+5ql7uTSICUghiuiGPLX\nVA3dMeifueKyYHyxJhug14moj2C4OlXXgSSjFmmpxkFfF0UBM/JS0dDWg1anK2L3m2h7jvYtCZYM\nPqg7Nz0JeQ4LDpY3o7MnshsEiGKN4Yoohvp3DA4OVzVNnUi16GEx6WIxLBqBKAjItJlR39c9P1xd\nvR40tvWicEAx+0D9dVfq7Hfl8fqW/dJTjWctewLAijmZkGQlEMCIEgXDFVEMZaWZoRGFQe0Yelxe\nNHe4kMdZq7iUZTfD7ZXRFoHZpKpAf6uUYV+fma/uuqvS8hb0uiUsHbIk6Ldslq/AnUuDlGgYrohi\nSKsRkZVmRnVTV2AmxN+xncXs8clfd1UbgaXByvrBzUOHys+0wKjXqHbH4O7AkmDGsK+np5owPS8V\nR6vaVL30SZFzqqMdt/zjLZzqUPfxSNpYD4BosstzWHCmsQvN7b1wWE2BYnbWW8Unf7iqb+nGnEJ7\nWNca2pl9KI0oYlpeKkrLW9De6UKqxRDW/SaSxyth33HfkuDQYv2BVszOxPHqduw6Uo9Ll+VP4Agp\nFG8cL8O3t3wIANAKIj6+8VZkmpPQ0N2Fa996DVpRRKrZiGJLKuakOzAnzYF5jgwk68/+2e32eLCl\nugrvVZ7EA+esQF5yCoxaLd6tLEeSTo//7+IrJvrbixiGK6IYy3MkYSd8Re0Oq2lAGwaGq3iUlda3\nY7A5AjNXdU4kGbVwDClmH2jmFCtKy1tw9HQbls3KDPueE+VQRSt63RIuWJg77JKg35KSDPzx/ePY\ncZjhSg1eLjuEdpcLs+zpkBQZetG3AOaVZbS6euCRZRxva8EOuTrwmf9Zezn+acYsAMBzB76AAgWf\nnK7ClupT6JV8fc7mOzJx29wFyDCZMdOWhrdOHMUjS1ei2Gqb+G8yAhiuiGIsN72/HcOi6Q6cafIV\nt+ekMVzFo0xbX7hqDS9cdfd60dDag1kFtlHDx8BmomoKV4HGoSMsCfqlmPWYU2THwfJm1DZ3IZs/\n93HLI0nYVXsGM21p+OTGWwe9lmNJxuHbvwkAsNrN2HGiCoeaGnGouRFLMrMB+ALYY9u2wC37AlWJ\nPQ2XFBTj0qKpWJzha80hCAIeXroSX3vvb/jl3l345YWXTuB3GDkMV0QxljfkGJwzTV1ISzHCZOAf\nz3hkNmqRkqQPe+ZqpM7sQxVmJUOvE1VV1O7xyth3ohFpKQYUZY/+/QG+pcGD5c3Yebge15xXPAEj\njB5ZUaAoCjRi4pU072usR7fXi3Nz80Z9n06jQYk9HSX2dPwTZg167cUrr0VNpxMrsnNRmGod9vNX\nTp2OGTY7Xjt2BA+eswL5KakR+x4mSuL97hOpTFqqEUa9BtWNXejs8aC9080lwTiXZTOhub0XHm/o\nR7eMVW/lp9WImJ6bipqmLnQM6eQfrw5VtqDHJWHJzOF3CQ61cHo69FoRO480QIlAi4tY+vIbL+G/\n9u6K9TCiIs1owt0Lz8GXi6eH9HmtKGJNXj5uLJkzYrACfC1PvrV4GbyyjF9/8Xmow40phiuiGBME\nAbmOJNQ1dwdmM9iZPb5lpZmhAGhoDf0YnMq6DgBjz1wBwIy+pcF46NYuyfKY79nTtyS4dIwlQT+T\nQYsF09JR39L/Z0CNqp0d2FNfh5Ntvr5kkizjgc3v4ePTpyLSFy3Wiq02/PDcNViTF/3auGunl+CG\nmbNx3fSZUb9XNHDdgSgO5DksOHmmA3uONgLgTsF4lzngGJxQW2acqnPCbNDCYTWN+d6B5wyOVcMU\nTYcqW/CLl/dh3tQ0XL2qCMU5Z/fn8koyvjjeBHuKYdjXR7JidiZ2lzVgx6F6FI7Q9yvebavxFXHP\nd/h+j7bXVuMPR0rxhyOlmGq14fY5C3BjyRykGILb9emSvDjQ2IADjfXY39iALHMSHl2+alyzgmql\nFUX86qLLYj2MkDFcEcUBf6f2z/v+xZ/HHldxLdwzBrt7vagfRzG7X1F2CnRaMeb9rnYcqoMC4MDJ\nZhw42Yx5xWm4enUhpub018QcrmxBt8uL1fOzg/rLf25xGswGLXYdqcf6L02DKKovOOzoC1fn5vhq\nklbn5uOdf9qA35Xux59PHMX3P/sYT+z8DGvy8vHC5VdDEATUd3fh/cpy2I0m2I1G2IwmdLhdONBY\nj1tmz4deo8Hpjg58+Y2XB91rbUExlmXnTNj3truuBg9+/D4ePGcFvjJtYmeTyttakWowIs009j9E\n4gXDFVEc8Be1d/Z4IKB/uz/Fp3DDVdUYzUOH0mlFTM1JwdGqNnT2eGJyLJKiKCgtb0GKWYdvXD0H\nf/msEgfLm3GwvBlzi+34yqoiTM1N7d8lODO4GTadVsQ5JQ5s2V+LY6fbUFKgvi3422qqkazXY05a\n/zmKizOzsTgzG4+duwYvHinFC4cOYEdtdSB4ljU34YGP3x/2ekuzcjDfkYliqw13LVgSuO6/ffQO\nnjmwZ0LD1WdnTqOspRniBM+WfVRVgZv+/hbuWXgOvr/yvAm9dzgYrojiwMClJYfNBINOE8PR0Fgc\nVhNEQUB9S2g1V5V149spONDMfBvKqtpw/HQbFs1wjP2BCDvd0In2LjdWzsnCrEI7ZhXaUXaqFX/5\nrAKl5S0oLW/B3CI7yms6YEs2oDg3+KW9FbOzsGV/LT7cU626cFXf1Yny9jaszS8adqdgusmMexcv\nw78tWgqnu39jwkx7Gp6+8FK09vaitbcHLb29MGo1mJeeibxk3zMUBQE/XnU+AF/IfebAHnx4qhLt\nrl6kGkbukRZJn53xzcqtyB59p2CkrczJQ5rRhN+W7sPdi86BzaiO2SuGK6I4YDHpYLXo0dbpZjG7\nCmg1IhxWY8gzV/6i7eEOMx7JwLqrWISrg+XNAIB5xf1d6UsKbCgpsOFoVSv+vLUCpRUtAIBz52WF\nNMMxM9+K4pwU7DnWiIraDhRlq6f2qsvjwRVF03DBlIJR3ycIwqCaq6wkC24smTPu+wiCgF9deBky\nk5ImLFi5JQm7686gxJ4Gh3liZ9VNWh3+deE5+NH2LXju4D48vHTlhN4/VNwtSBQn/LNXbMOgDpl2\nMzp7POjs8QT92VN1TpgMGjhs4/9XeHFOCrQaIWZ1V6XlLRAAzC46+8ifmfk2PHLTYnz7pkVYuyQP\nly8fPWCMRBAE/NMaX5+rN7eUhzPcCVdsteH/Lr8at81dEPV7zUl3IN00cSHni4Y6X3+rnImdtfL7\n6tz5sBuNePbAXjjd6jiDkuGKKE746664U1AdQq276nF5Ud/SjYLM5KBmd/Q6DYqzU1BV70RLR29Q\n9wxXj8uLE2faUZidjBSzfsT3zcy34aaLZ8CWHPoZiLMK7ZhVYENpRQuOVrWGfJ1E55EkvHm8DH89\neSzq99rWtyS4KndK1O81HItOj2/MX4I2lwvPl+6PyRiCxXBFFCe+tCgXFyzKxaJpE7/kQ8EbeIBz\nMKrqnVAw/mL2gZbPyYIC4P+9uj+kGbNQHTnVCklWMK84bULud13f7NUbW8pV0VS0uacHt779Z/y9\n/PiE3bPd7cK9H72Lx7d/Oq7eY+FYmp2NW2bPw8oYzVwBwJ3zFiLdZEKba2L/YREqhiuiOJFhM+PW\nS2fCoGcxuxqEOnM13s7sw7lgYQ7WLsnDmaYu/L9X96HH5Q36GqHw11vNnaBwNTU3FQunpeN4dTsO\nlrdMyD3Dsb2mGu9UnMTRluYJu2e6yYzrZ8xCZUc73j9VEdV7rc7Nx88vuHhClyKHSjEYsPvmr+EH\nK9fEbAzBYLgiIgpBZojhqjJwpmDwxdqCIODGtdOxal4WKmqdePpPB+D2hH4Ez3j4WjA0I8moHdc5\ngZFy7ZpiCADe2HIy7rub76j1LZtN9MzOv8xfDAB4Zv+eCb1vrCTpfC1IFEWJ+58JhisiohBYLXoY\n9JqQZq6Meg0ygihmH0gUBNx2eQmWzHTg6Ok2/M9bpfBK0VsWqm3uRnOHC7ML7RN6GPGUDAuWzc5E\nVX0n9vadXBCvttVUw6DRYFFG1oTed1ZaOs7PK8BnNdU42NQQlXv8+ovduPKNl1HW0hSV6wfraEsz\nrnrzFbx69HCshzIqhisiohAIgoAsmxn1LT2Q5fH9K7rH5UVdc/DF7ENpRBH/ctUczC2yY//JZjz3\nt8PjHkOwSgNLgmfvEoy2a1YXQRQEvPlpedS+v3C19fbiUFMjFmdmwaid+O5G31iwCADwv/v3RuX6\nm0+fwq66mpguCQ6kFUXsqqvBe5XxvZuU4YqIKERZaWZ4JXncu/dON3SGXMw+lE4r4u7r5mF6Xip2\nHWnA7989GpXi7/7+VhNTbzVQpt2M1fOzUNvcje2H6ib8/uOxq64GCiZ+SdDvwvwiXFJQjKVZke/W\n7pK8+LyuBrPsaXETropTrShMScXHp0/BI0V3STwcDFdERCEKtqg9lM7sozHoNPjW9QuQn2nBlv01\neG3zyYgGLJdbwtHTbZiSYYHVEnp7hXBcvaoIWo2AP2+tiOryZ6gMGg3Oy52C83LzY3J/URDwhy9f\ng1vnzI/4tb9oqEeP14tzY9SCYTiCIGBtQRE6PW7sqquJ9XBGxHBFRBSiTLuvbmrc4aq2A0BkZq78\nzEYtHrhhIbLTzHhnVxVe/SByfY+Onm6FV1JisiToZ08x4oJFuWhq78WW/eH/ZdrS0QuXO3IzHudP\nKcDrX1kXsx5QA3V5PHBJkdtBuu3MaQDAqpzYf28DXZRfBAD4IMq7JMPBcEVEFKJsu6/h63jClVeS\nceBkM6wWfWCnYaSkmPV48IaFSEsx4g/vlOGdnVURua6/DcK8oolfEhzoyysLYdBp8NfPKuEKY3dk\nVb0T33lmO/74fvQbb060j6oqsej3/4vXj5VF7Jqf1cRmF+RYzs3Ng1GjwYdVDFdERAnHv+NvPI1E\nSyta0O3yYtmszLCK2UdiTzHi4Q0LkZ5qxKubT+DdXeEHrNLyZhj0GkzLS43ACEOXmqTHxUvz0N7l\nxkd7qkO6hiTLeP7tMnglJXC2Y7h21J7BPR++g30Nsa8HK7GnodPjwTP790Zsafj66SX45oIlSDPF\n12HJJq0O9yxaiq/OmR+3LRlC2trQ29uLhx9+GM3NzUhKSsJTTz0Fu33wtPFdd92FtrY26HQ6GAwG\nPPfccxEZMBFRvDAZtLBa9OOaudp1pB4AsGxWZtTGk2Ez4yf/ugrf+fVWvPLRCQgALlkWWi1QQ2s3\n6lt7sGh6OrSa2P87/LJl+fhozxn8Y8cpnL8wF2ZjcH99vbvrdKCBa0NbDxRFgRBmyP3oVAVePXoY\n106bGdZ1IiHHkoyrp07HG8ePYkt1Fc4f4wDp8dgwa24ERhYdjyw7N9ZDGFVIf2JeeuklzJgxAy++\n+CKuueYa/OY3vznrPVVVVXjppZewadMmBisiSlhZdjOaO1yjNvN0eSR8cbwJ6anGqDfizEm34JEN\ni2C16PHyRyfw3u7TIV0nsCQYg12CwzEbdbh8RT66er14fcvJoD5b29yFtz6tQEqSHjOmWOFyS3BG\n4Pig7bVnIAoClmVHfqdeKPqbikanLUM8itfjkUIKV3v27MF5550HAFizZg22b98+6PWmpiZ0dHTg\nrrvuwoYNG7B58+bwR0pEFIcCZwy29oz4noMnm+FyS1g+OzPs2ZLxyLSb8chNi5Fq0ePlD4/j/c+D\nD1iB/lZFsStmH2rtkinISU/C5r1n8N44lz1lRcH/vV0GryTj5otnBHZqNo7y+zUePV4Pvqivw7z0\nDCTrY7OTcqjFmdk4JzMbH1RVoK6rM6xrffXtP+Of//4mvFE+tzAcj237BAt//2xEi/gjZcx51dde\new0vvPDCoK+lpaUhOdn3A5qUlASnc/D6tcfjwR133IFbb70V7e3t2LBhA+bPn4+0tJH/BWSzmaHV\nhn+mmsMxccczJCo+w/Dw+YVPTc9war4NH++rQY9XGXHc+/5xBABw6blFE/K9ORzJcDiS8dO71iVm\nxgAAIABJREFUV+N7//MZXvrgOJItRlx1XvG4Pu/xSig73Ya8DAtmTc+I8miD8/hd5+Lhp7fglc0n\nUJBnxeoFuaO+/29by3G8uh3nzs/G5edNxd8/qwB2n0avNPLvFzD2z+DHlZVwyxIumjoxv6fjdfPC\n+fj83VrsaavHbYXZIV3D5fVi8+lTmG63IzsztHq7iXgmRqMOtV2dKOtuxdri8f1sT5Qxw9W6deuw\nbt26QV+755570NXVBQDo6upCSsrgM7LS09Nx4403QqvVIi0tDbNmzUJFRcWo4aq1NbgjJIbjcCSj\nsTEyhYqTFZ9hePj8wqe2Z2jpO2j7aGUzZuSc/RdKj8uL3YfrkZ1mRpJWiPr3NvD5GQTgwRsW4mcv\nfoH/fesgurpcuGjJ2Du/Dle2wOWWMCvfFne/FwKAe/9pPn76x734+R/3AF4JM/Ntw763qa0H//e3\nw0gyarFuTTEaG50wa30zhyerWjEn3zrs58bzM/iPw74dhwtsGXH1jC7OLsKH627G3HRHyOPaUVON\nXq8XyzNzQrrGRP0ZXunwBes/HTiEBcmOqN9vqNECZEjLgosXL8Ynn3wCANiyZQuWLFky6PVt27bh\nvvvuA+ALX8ePH0dxnKVKIqJICDQSbR7+H4j7jjfB45WxbNbELAkOlZ2WhEduWoSUJD3++P4xfLR3\n7N12/V3Z42dJcKD8zGTcfe08KArwq9cP4kxT11nvURQFL7xTBpdHwo0XTUdqXxNUR98Oz4a28JYF\nM8xJWOjIxIrs0WfOJprDbMY8R0ZYP2v+Fgzx0LtrNCtycmHW6vBhHPa7CilcbdiwAcePH8eGDRvw\nyiuv4J577gEA/OxnP8OBAwdw/vnno6CgAOvXr8edd96JBx544KzdhEREiSDdaoRGFFA/wux7/y7B\n2C2vZacl4ZENi5Bi1uEP7x3DH987Nmq/qNLyFui1ImaOMLMTD+YU2XHb5SXodnnxX6/uQ6vTNej1\nrQdrcaiyFXOL7Th3bv+BymkpRghC+OHqq3Pm4711/wybMb7aFAC+YHmwqQHlba0hfd7fPDTe+lsN\nZdBosWZKPk60taKivS3WwxkkpFYMJpMJTz/99Flff+SRRwL//b3vfS/0URERqYRGFJFhM6Guufus\n7f2dPR6UVrQgP8OC7LSkGI4SyElPwiM3LcZ/v3kQH+6txuFTLfjalbNRlD24rKOloxdnmrowrzgN\nugjUwUbTqnnZaHG68OaWcvzytf349j8vhsmgRVunC698eAIGvQZfvbRk0O+JViMiLcUYdkF7PNtZ\newZXv/Uqbp+7AE+tuSioz7okL3bX1WB2WjrscRgch1qbX4R3Kk7io6oK3DlvUayHExD75iVERCqX\naTOj2+U9a3v/3mONkGQFy2ZHr7dVMHLSk/DD25Zi7Tl5qG3uxhOb9uCvn1VAGrAjrLTC14Ihlkfe\nBOPKlQU4f2EOqho68Zu3SuGVZGx69yi6XV6sv2Aq0lKNZ33GYTWhvcsd8jE4L5cdwvc+3Yzazvip\ntRpoSWY2Ug0GvF9ZHnSrAo8k46GlK3H73IVRGl1kXVxQhIeXrozZ2Y4jYbgiIgpTVtrwdVeBJcGS\n+Nlxp9dpcNPaGXjwxoVISdLjzU8r8NM/7A0sa/bXW8VHf6uxCIKAmy+ZgQVT03CoogVP/mEPvjje\nhBlTrDh/0fD1UP7O+o0hLg2+ebwMzx78AloxPmf2dBoNLpxSiOpOJ460NAX1WYtej3sXL8NXo3AQ\ndDRkW5Lx8NKVmGGPr59XhisiojAFel0N6NTe3uXGkVOtmJqTgnRr/C2vzCm048d3LsPy2Zk4WdOB\nH/5uFzbvrcbhyhakpxqRaYu/MY9EI4q46ytzUZSdjIpaJ3RaEbdfXjLiMUMZ1tDDlVeWsauuBtOt\ndjjMkT0jMpIuLvRtInu/cvzF3rKioMcbfnPVWFDibOwMV0REYQrsGBwQrj4va4CiRPe4m3AlGXX4\nxtVz8I2r50Aritj03jH0uCTMm5oWk52N4TDoNfjW9QuwcFo6br+8ZNTDsR3W0HcMHmxsQJfHE/fF\n3hflF0IUBLx3qnzcn9lSXYUFL/wv3jp+NIoji7zy9lYs2fQcfrz901gPJYDhiogoTJnDhKtdR+oh\nADgnjpYER7J8diZ+fOcyzC60QQCwdGb8j3k4KUl63Hv9fKyYkzXq+zLCaMewra9Nwcqc+GrBMJTN\naMKyrByUtTShyzO+GZ3nS/ehzeVC/pDelfFuiiUF7W4XPjhVETfH4YS0W5CIiPqlmHUwGbSBcNXS\n0Yvj1e2YOcUKW3J8HI0yFnuKEQ/esBDObg9SkvSxHk5U+WeuQtkxuKPWH67ie+YKAP577eXIMJth\n0Iz9V/0ZpxPvVpZjgSMTizJGD6fxRqfR4IIpBfjryeM42daKabbYb8bgzBURUZgEQUCW3YSG1h7I\nsoLdZQ0AEDe7BMdLEISED1YAYDJokWzWhTRzVZRqw3l5+cixxM+RNyOZkpwyrmAFAJsOH4CsKLh9\n7gLVLQkDvpYMAPBBVXw0FGW4IiKKgCy7GZKsoKm9B7uO1EMUBCyZOfFHctD4ZFhNaG7vHdSGYjx+\nvOp8vH719VEaVeTVdjrx+0MH4JFGbjvhliRsOnwQqQYDrpk2cwJHFzkX5hcCAD48VRnTcfgxXBER\nRYC/qP1geQsqap2YXWhDijnxZ4HUymE1QZIVtHS4xn6ziv3qi9146JMPsLPuzIjv+eT0KTT2dOPG\nkjkw63QTOLrIyUyyYF56BrbXVKPT4471cFhzRUQUCf6i9n/sOAUgvncJ0uAdg444bJURKRcXFOO5\ng/vwbmU5Vo/QaHNtQRH+cd2NyEqyTPDoIuvby86FRhCgj4P+Y5y5IiKKAP/MVavTBa1GwOIZ6TEe\nEY0m0Eg0gY/BAYBzc/Ng1urwfuXILRkEQcA5WTnIS1bXLsGhLiksxkUFRdBrGK6IiBJCpq2/r9K8\n4jSYjepcXpksHGE0ElUTg0aLC6YUoLy9DSeHOcj5LyeOoSzILu7xrqG7K+YtGRiuiIgiwKDXwJ7i\na7vAJcH4F06vK7W5tK9b+3tDZq86PW7ct/k93PDXN4Iu7I9XG7d/irn/9wzKWppjOg6GKyKiCJma\nkwqLSYcF0+LrnDM6W2qSHnqdmPDLggBwUUERtKKIM50dg77++rEydHrcuGX2PGjExIgDCzIycd30\nmYh1NwkWtBMRRcgdX54Ft0eCUc//tcY7QRDgsJrQ0NYDRVFU2dtpvDLMSTh6xzeRrO9vaKsoCp4v\n3QetKOLm2fNiOLrIumrqDFw1dUash8GZKyKiSDHoNEhm+wXVyLCa0OuW4OyJnwN/o2VgsAKAXXU1\nONzchCuKpql+l2A8YrgiIqJJKZxjcNRGkmX87eRxbDp8AADwfOl+AMDtcxfEclgJi+GKiIgmpYG9\nriJF8HZA8HaM/cYJJgoCvrv1I2zcvhWSLCPVYMBCRybOVcEZiWrEwgAiIpqUIt3rSuypgnXXWoje\nVrgcV6I39xZ47OcDQuz7LgmCgIsLirHp8EF8Xl+Lp9ZcBDnBa81iiTNXREQ0KWVEcOZK8HYgdd96\naNx1kPUZMNa/Duvea2DfOg/mExshdsf+QOFLC6cCQKChqMhgFTUMV0RENCmlpRohCBFoJCp7kXLg\nNmg7D6Nnyr+gZXUpWpd+gJ7c2yB42pFU8TOkfbYAqZ9/GYaalwCpOzLfQJBW504BADz9xW7sqq2J\nyRjGJHuhb/g7oIx80LQaMFwREdGkpNWISEsxhjdzpSiwHH0E+uYP4Eq/BJ0zfgoIArzWZeic/TSa\nzz+GjjnPwG07D/rWT5Fy6Buw7r0mct9EEMw6HeakOQAAJm18VgUZzzyP1P0bYKh/K9ZDCUt8Pl0i\nIqIJ4LCacORUK1weCQZd8LVRptP/A1P1c/Ba5sA573lAHPLXqiYJrpwNcOVsgNhdgZRD34CubQc0\nXScgJU2L0Hcxfi9deS1OtrViniNjwu89HvqmDwAAms4jMR5JeDhzRUREk1agqD2E2St949tIOvoo\nJH0m2he+CkWbPOr7ZXMRenNu9n226d3gBxsBWUkWrOpbHow7she6ts8AAJqekQ+aVgOGKyIimrQy\nQux1pXEeQMrBOwDRiI6Fr0A2jS+wuNMvARC7cBXPtM4vIPa1sdB0M1wRERGpUii9rsTeGqR+sR6Q\nutEx7zl4UxeP+7OyIQuelEXQtW6Ny35YsaRr2RL4b4YrIiIilQo6XHk7kbLvBmhcNeia/mO4M64K\n+p7u9MsgKF7omj8K+rOJTN/yCQDAk7oUorcNgqclxiMKHcMVERFNWsE2Em3fcid0zv3oyf0qegru\nDemebsdlAABD4zshfT4hSb3Qte2A1zIXntRlANQ9e8VwRUREk5bJoIXFpBvXzNWZpi50t51Gmfc8\ndJb8AgixCac3eQEkfSb0Te+pvp9TpOjad0GQe+G2nw/JXAyA4YqIiEi1MmwmNLf3QpaVUd+37WAt\nvnfscZws/B0g6kK/oSDCnX4pRE8TtO17Qr9OAtG1fAwA8NjXMFwRERGpXYbVBElW0NLRO+J7JFnG\ntkN1MBt0WDQ9Pex7+pcGuWvQR9+yBYqggce2CpKpL1ypuB0DwxUREU1q4ylqP1zZivZON5bNzoRO\nG/5BzG77BVAEPcMVfOcyajv2wJuyGIo2BbJxChRBy5krIiIitfIXtY8Wrj47WAsAWDUvKzI31Vrg\nsZ8HnfMAxN4zkbmmSulat0FQJLjt5/u+IGohmQoYroiIiNTKMUYj0e5eD/Yea0KW3Yzi7JSI3deV\nzqVBAND5WzDYLwh8TTIVQ/Q0QfC0x2hU4WG4IiKiSW2sZcFdRxrglWSsmpcFIcQdgsNxOy4FAOgn\neUsGfcsnUERjoAUDgP6i9p6KWA0rLAxXREQ0qVkteui14ogzV5+V1kIAsHJOhJYE+8imQniTSqBv\n+RiQuiN6bbUQ3E3QdpbCY10BaIyBr8sq3zHIcEVERJOaIAhwWE1oaOuBogxux1Db3IWTZzowu8gO\ne4pxhCuEzu24DILcC/2Ao18mE//37bGvGfR1te8YZLgiIqJJz2E1odctobPHM+jr20rrAESwkH0I\n9ySvu/KfJxgoZu/jXxYUu7ksSEREpErD7RiUZQXbSutgMmiweLojKvf1pC6DrLX6wpUyehPTRKRr\n+RiyNgXe5EWDvi6Z8qFA5LIgERGRWg23Y/BIVStanS4sLcmEXhd+b6thiVq40y+Gprcams5D0blH\nnBJ7TkPbUw6PdRUgaoe8aIBsnMJlQSIiIrUabuYq4r2tRuBO9+0aNDRNrl2Duta+equ084d9XTIX\nQ+OqBaSuiRxWRDBcERHRpJcxZOaqx+XF3qONyLCZMC03Nar3dqevhQJxXC0ZBE8LxJ7KqI5nouj7\nzhN020YOVwCg6a6coBFFDsMVERFNemmpRghC/8zV7rIGuL0yVs2NbG+r4Sg6OzzWFdC274bgbhrx\nfbqWLbB/dg5sO9YAsmfE96mCokDXsgWyLh2SZfawb1HzjkGGKyIimvS0GhH2ZGMgXG076Ottde7c\n7Am5v9txGQQo0De9f/aLigJT5dNI3XM1RE8TRG8bNN3HJ2Rc0aLpPg6NqxZu+xpghPAqqbjXFcMV\nERERfHVX7Z1uVNZ24Fh1O0oKbEhLjXxvq+GM2JLB24nkg7fBcvz7kPUZ6M3eAADQOg9GZRyC1wmN\nM/qF9cMdeTMUwxUREZHK+XcMvvhuGYDoF7IPJCXNhGQqhL75g8CSn6brOGy7LoSx/k14rCvRuuJT\n9ObcAgDQOkujMg5z+VOw7VgFTWdZVK7vp+8LV0P7Ww0kmQoBcFmQiIhItfw7BrcfrIVBr8GSGRkT\nd3NBgDv9EojeDujatkPf8HdYd30J2q4ydE+5C21L/gbFkAlv8hwAgLYzOuFK010OATIMjX+LyvUB\nAIoEXcsWSMZ8yH0BavjBmCAZcjlzRUREpFb+HYMAsHRmBgz6KPW2GoGrb2nQcuR+pO7fAEH2oGPu\ns+gq+Rkg6gAAis4GyTgFmijNXImeZgCAvvHtqFwf8C1pit4236zVGJsFJHMxxN5qQOqN2niigeGK\niIgI/cuCwMQuCfp5bKuhaJKg7T4OyVSI1mUfwpV9w1nv81rmQuOuh+BujPgY/LsVte2fR+X6wMB6\nqzVjvNMXrgQo0PScispYooXhioiICL5lQQFApt2M6VOsEz8AjRFd0/4dPbm3oXX5J5CS5w77tsDS\nYBSK2v0zVwIU6Bujc96hPhCuRq638lNrOwbt2G8hIiJKfCaDFl+7cjZmFKVBjHJvq5H05P/rmO/x\nJs8DAGidh+BJuzByN1ckCJ5WSIZsaFy1MDS9DVfuzZG7PgDIbuhat8GbNBOyYezZwf4dgycjO44o\n48wVERFRn5Vzs1BSaI/1MEYlWXwzWtrOyM5cCZ5WCFDgTV0Kr3ka9M0fRbzWSdf+OQS5e1yzVoB6\n2zEwXBEREamIZC6GIpoi3o5B7Ku3knXpcDsuhyB1Qd93/l9EKApMp34NAHDbxzfjJpmKAKhvWZDh\nioiISE0EDbyW2dB0HQVkd8Qu66+3kvV2uNMvBxDZXYOGuldgaPwb3LbVcDsuG9+HtBZI+kzOXBER\nEVF0eZPnQVA80HQdi9g1/TsFFX06PNYVkLVWX8d4RQn72mJvDSxlj0DRJME5+78BYfzxQzYXQ+yp\nimiQjDaGKyIiIpXx9u0kjOSOwcDMlS4NELVwp18MTW81NOHWdikKLIf/DaK3DZ0zfgLZXBTUxyVT\nEQTI0PRUhTeOCcRwRUREpDL9Re2Rq7sK1Fzp0wEAbodvadAQ5tKg8czvYWh+H+60C9Gbe3vQnw8U\ntauo7orhioiISGW8Fn+vq8iFK6Fv5krRpQEA3GlroQjasOquxJ5TSDr2KGRtKpyzfz1mR/bh+MOV\nqKK6q7DC1fvvv48HH3xw2NdeffVVXHfddVi/fj02b94czm2IiIhoAEWXCslYENWZK0Vnhce2CrqO\nvRB7a0MYpIzkQ3dDlDrROfOnkI15IY2rv5FoRUifj4WQw9XGjRvx85//HLIsn/VaY2MjNm3ahJdf\nfhm//e1v8Ytf/AJut3oK0YiIiOKdN3kuRHcjBFd9RK43qOaqj7vvvEN9U/Dd2o2nn4W+dQtcjivg\nyr4p5HFJfTVaatoxGHK4Wrx4MR577LFhXztw4AAWLVoEvV6P5ORk5Ofno6ysLNRbERER0RD9S4OR\nKWoX3M1QNEmApv+MRZcjtJYMmq4TsBz/IWSdDc5ZvwxpOdBP0dkg6+yqCldjHn/z2muv4YUXXhj0\ntSeeeAJXXHEFdu7cOexnOjs7kZycHPh1UlISOjs7R72PzWaGVhv+CeQOR/LYb6JR8RmGh88vfHyG\n4eHzC58qnmHPMqACsCrHAce14V9PagGM6UO+9wXAwVkwtH4Mh00LaE0jfjxAlmA/dg8gd0NY+TzS\n86aFP7aU6RBb98KRZgbE8LNCtI0ZrtatW4d169YFdVGLxYKurq7Ar7u6ugaFreG0tnYHdY/hOBzJ\naGx0hn2dyYzPMDx8fuHjMwwPn1/41PIMRWUq0gD01u2B0xHmeBUF6b2N8FpmoW3I955kvxTmjv9C\n+7G/BnYQjsbR9AzQtA29mdfBab4ciMCzTNYVwCjvRHP1EcimgrCvFwmjBfCo7BacP38+9uzZA5fL\nBafTiZMnT2LGjBnRuBUREdGkJJuKoGiSIlPULnVBkHsDOwUHcgW6tb8z5mU0nYeB/d+HrM9AZ8nP\nwx+Xf3gqO2NwzJmrYDz//PPIz8/HRRddhFtuuQU33XQTFEXB/fffD4PBEMlbERERTW6CCK9lNrQd\nXwCyCxBD/3u2/+ib9LNe81qXQdbZoW96B1DkEburazqPInXPVwDZBeesp6Hozw5qoRoYrjxpX4rY\ndaMlrHC1fPlyLF++PPDr22/vbw62fv16rF+/PpzLExER0Si8lnnQte+GtrMM3pQFIV+n/9DmYQKR\noIE7/VIYa1+CtmMfvKmLz3qLprMM1j1XQnQ3AEt+Bbf9ipDHMpz+dgzqmLliE1EiIiKV8h+Dowlz\naXC0mStgwK7BprN3DWo6D8P6+RUQ3Q1wlvwCmHlPWGMZjmSe6ruXSpYFGa6IiIhUyps8D0D4ndoD\nhzbrhg9XnrQLoQi6s+quNM5SWD//MkRPE5yzfoneKV8LaxwjUXR2yNpUhisiIiKKLskyG0D4ZwyK\nnhYAgDxCnZSiTYHHtho6536IvWcAABrnAVj3XAnB0wLnrF+hNy/4cwPHTRAgmYp9XdqVs5uXxxuG\nKyIiIpVStMmQTIW+RqKKEvJ1hh59M5z+hqLvQNuxD9bPr4TgaYVz9n+jN++rId97vCRzEQS5F6Ir\nhKN4JhjDFRERkYp5k+dB9LRAdNWFfI2hhzYPx9/jylT9W6TuuRqCtx3OOb+BK/fmkO8bDDW1Y2C4\nIiIiUjGvxVfUru0M/Ric/pmrkcOVbCrwtX7oLIXg7YBz7jNw5fxzyPcMlpqK2hmuiIiIVCywYzCM\nonbR0wxF0EDRWkd9X2/WeiiCFs65/wtX9o0h3y8UamrHENEmokRERDSx+meuQg9XgrsJis4+YoNQ\nv57C+9E75WtQtCkh3ytUsrkIAGeuiIiIKMpkUwFkTXJY7RhET/PwDUSHEoSYBCsAkPWZUEQzwxUR\nERFFmSBCssyGpvs4IPUG/3nZC9HTOupOwbggCJDMxb5lwTB2Rk4EhisiIiKV8ybPhaBI0HYdCfqz\ngrcVwOg7BeOFZC6GIHVBcDfEeiijYrgiIiJSOX+ndo3zUNCfHU+Pq3ihlnYMDFdEREQqF047BtHd\nd66gzh7RMUWDf8egtvtEjEcyOoYrIiIilfNaZkOBEFJRu+DpO1dQBTNX3pQFAABt++4Yj2R0DFdE\nRERqp7VAMhWFdAxO/8xV/NdceS3zIGuSoWvdGuuhjIrhioiIKAFIyfMgetsgumqC+pzoUU/NFUQt\nPNYV0HafgOCqj/VoRsRwRURElAD8ndq1zuDqrgT/zJUawhUAj20VAEDf+lmMRzIyhisiIqIEEGqn\ndv/MlRpaMQD94UrXxnBFREREURTqGYNqqrkCAG/KIiiiCTrOXBEREVE0ycZ8yNrUoGeuBE8zZI0F\n0BijNLIIE/XwWJdB23k4sKQZbxiuiIiIEoEgwGuZA03XCUDqGffHRHeTKtowDOSx+pcGt8d4JMNj\nuCIiIkoQknkaBMjQ9Jwa3wcUpe/Q5vhvIDpQoO4qTpcGGa6IiIgShGQuAgBoeirG9X5B6oQgu1Sz\nU9DPk3oOFEHPcEVERETRJZuCDFceX82SWnYKBmhM8KYugdZ5AIKnPdajOQvDFRERUYLwz1yJ3eML\nV2o6tHkot20VBMjQte2I9VDOwnBFRESUICRTIYDxz1yprQ3DQP39rrbFeCRnY7giIiJKEIrOBllr\nhWacM1dqOrR5KE/qciiCJi7PGWS4IiIiSiCSuci3W1CRx3yv6G4BoM6ZK2gt8CYvhLbjC0DqivVo\nBmG4IiIiSiCSqQiC4h7XAc6qOrR5GB7bagiKF7q2XbEeyiAMV0RERAlEMhcDwLiWBv0dzhWV9bny\n89jOBRB//a4YroiIiBKIHERRu+pnrqwroUBguCIiIqLokfp6XYk9lWO+V3Q3QxE0ULSpUR5VdCg6\nK7zJ86Dr+ByQemM9nACGKyIiogQS6NLeXT7mewVPk6+BqKDeOOCxngtBdkHXsSfWQwlQ79MkIiKi\ns8iGHCiCfnzLgu5m1S4J+nlsqwHEV90VwxUREVEiEURIpoKxC9plD0RvmzrbMAwQj0XtDFdEREQJ\nRjIXQfS2QfC0jvge/2tqn7lS9OnwJpVA17YTkD2xHg4AhisiIqKEIwUOcK4c8T3+nYJqbcMwkMe2\nCoLc7WsoGgcYroiIiBKMHChqH3lpMHCuoMpnroD4O2eQ4YqIiCjB9LdjGDlc+c8VVHvNFQB4rH3h\nKk7OGWS4IiIiSjCBZcFxzFyp8dDmoWRjNrymYujadgCKFOvhMFwRERElGslUAGCMmit34sxcAb6l\nQdHbAa2zNNZDYbgiIiJKOBoTJEPOqL2uBE/i1FwBA1syxH5pkOGKiIgoAUmmIoi91YDsGvZ1/8yV\nkjAzV33NROOgqJ3hioiIKAHJ5kIIUKDpqRr2dTEwc5UY4Uo25kMy5vmaiSpyTMfCcEVERJSA+ova\nhz9jUHQ3Q9YkA6JhIocVPYLgq7vytEDTdTSmQ2G4IiIiSkD97Rgqh31d8DRBSZBZK79AS4b2z2M6\nDm1M705ERERRIfkbiQ5X1K4oEN3N8CbPm+BRRZcr6zpoOw/C3Vd/FSsMV0RERAlotF5XguSEoLgT\nZqegn6JNQWfJz2M9DC4LEhERJSJFZ4esTRl25kpIoAai8YjhioiIKBEJAiRTka+RqKIMeklMoKNv\n4hHDFRERUYKSTUUQ5F6IrrpBX0+kQ5vjEcMVERFRghqpqN3fnT1RGojGG4YrIiKiBCWZCgEA4pBw\nxZmr6GK4IiIiSlAjNRLtr7myT/iYJgOGKyIiogTVvyxYOejrAmeuoorhioiIKEHJxjwogu6smqtE\nO7Q53jBcERERJSpBA8mUf1YjUdHTDEXQQtGmxmhgiY3hioiIKIHJpkKInmYI3o7A1wR3k6/HlSDE\ncGSJi+GKiIgogQ13gLPoaWF39igKK1y9//77ePDBB4d9bePGjbjuuutwyy234JZbboHT6QznVkRE\nRBQCyVwMYMAZg7IHoreN3dmjKOSDmzdu3IitW7di1qxZw75+6NAhPPfcc7Dbuc2TiIgoVgLtGPqK\n2gVPCwDuFIymkMPV4sWLsXbtWrzyyitnvSbLMk6dOoUf/OAHaGpqwvXXX4/rr79+1OvZbGZotZpQ\nhxPgcCSHfY3Jjs8wPHx+4eMzDA+fX/gS6hnq5gD7AYtSDYsjGWirBAAYU7JgjNL3mVAdChzWAAAH\n4klEQVTPLwRjhqvXXnsNL7zwwqCvPfHEE7jiiiuwc+fOYT/T3d2Nm2++GbfffjskScKtt96KuXPn\noqSkZMT7tLZ2Bzn0szkcyWhs5PJjOPgMw8PnFz4+w/Dw+YUv4Z6h5IADgLvlGNobndC1VMEKoEtK\nQXcUvs+Ee34jGC1Ajhmu1q1bh3Xr1gV1Q5PJhFtvvRUmkwkAsGLFCpSVlY0aroiIiCgKNEmQ9JmB\nRqL+HldcFoyeqOwWrKysxE033QRJkuDxeLB3717MmTMnGrciIiKiMcjmIoi9pwHZw0ObJ0DINVfD\nef7555Gfn4+LLroIV111FdavXw+dToevfOUrmD59eiRvRUREROMkmYqga9sBsbeKM1cTIKxwtXz5\ncixfvjzw69tvvz3w31//+tfx9a9/PZzLExERUQRIpkIAvnYM/Yc2c+YqWthElIiIKMENPMDZf2gz\nm4hGD8MVERFRghvY60rsq7mSdexDGS0MV0RERAluYJd20d0MWZsKiPoYjypxRbSgnYiIiOKPokuH\nokmCpqcCgrsZCmetooozV0RERIlOECCZivoK2pu5UzDKGK6IiIgmAclcBEHuhqB4uFMwyhiuiIiI\nJgF/UTvAHlfRxnBFREQ0CQwMV+zOHl0MV0RERJOAZC4M/DdnrqKL4YqIiGgSGLQsyJmrqGK4IiIi\nmgRk4xQoggYAoOgZrqKJ4YqIiGgyEHWQjVMAcFkw2hiuiIiIJgn/0iCXBaOLHdqJiIgmiZ682yDr\n0yGbCmI9lITGcEVERDRJuDOvhTvz2lgPI+FxWZCIiIgoghiuiIiIiCKI4YqIiIgoghiuiIiIiCKI\n4YqIiIgoghiuiIiIiCKI4YqIiIgoghiuiIiIiCKI4YqIiIgoghiuiIiIiCKI4YqIiIgoghiuiIiI\niCKI4YqIiIgoggRFUZRYD4KIiIgoUXDmioiIiCiCGK6IiIiIIojhioiIiCiCGK6IiIiIIojhioiI\niCiCGK6IiIiIIkgb6wFEgizLeOyxx3D06FHo9Xps3LgRBQUFsR6WKuzfvx//+Z//iU2bNuHUqVP4\nzne+A0EQMH36dPzwhz+EKDJ/j8Tj8eC73/0uzpw5A7fbjW9+85uYNm0an2EQJEnC97//fVRUVECj\n0eDJJ5+Eoih8hkFqbm7Gddddh9/97nfQarV8fkG65pprkJycDADIy8vDDTfcgJ/85CfQaDRYvXo1\n7rnnnhiPML4988wz+Oijj+DxeLBhwwYsW7Zs0v8MJsR3+8EHH8DtduOVV17Bgw8+iJ/+9KexHpIq\nPPvss/j+978Pl8sFAHjyySdx33334cUXX4SiKPjwww9jPML49pe//AVWqxUvvvginn32WTz++ON8\nhkHavHkzAODll1/GvffeiyeffJLPMEgejwc/+MEPYDQaAfDPcbD8///btGkTNm3ahCeffBI//OEP\n8fOf/xwvvfQS9u/fj0OHDsV4lPFr586d+OKLL/DSSy9h06ZNqKur488gEiRc7dmzB+eddx4AYOHC\nhSgtLY3xiNQhPz8fv/rVrwK/PnToEJYtWwYAWLNmDbZt2xaroanCZZddhm9961uBX2s0Gj7DIK1d\nuxaPP/44AKCmpgbp6el8hkF66qmncOONNyIjIwMA/xwHq6ysDD09Pbjjjjtw6623Yvfu3XC73cjP\nz4cgCFi9ejW2b98e62HGra1bt2LGjBm4++67cdddd+GCCy7gzyASJFx1dnbCYrEEfq3RaOD1emM4\nInW49NJLodX2rwwrigJBEAAASUlJcDqdsRqaKiQlJcFisaCzsxP33nsv7rvvPj7DEGi1Wnz729/G\n448/jksvvZTPMAhvvPEG7HZ74B+XAP8cB8toNOLOO+/Eb3/7W/zoRz/Co48+CpPJFHidz3B0ra2t\nKC0txS9/+Uv86Ec/wkMPPcSfQSRIzZXFYkFXV1fg17IsDwoNND4D18S7urqQkpISw9GoQ21tLe6+\n+27cdNNNuOqqq/Af//Efgdf4DMfvqaeewkMPPYT169cHlmkAPsOxvP766xAEAdu3b8eRI0fw7W9/\nGy0tLYHX+fzGVlRUhIKCAgiCgKKiIiQnJ6OtrS3wOp/h6KxWK4qLi6HX61FcXAyDwYC6urrA65P1\n+SXEzNXixYuxZcsWAMC+ffswY8aMGI9InWbPno2dO3cCALZs2YJzzjknxiOKb01NTbjjjjvw8MMP\n4/rrrwfAZxist956C8888wwAwGQyQRAEzJ07l89wnP74xz/iD3/4AzZt2oRZs2bhqaeewpo1a/j8\ngvCnP/0pUKdbX1+Pnp4emM1mVFVVQVEUbN26lc9wFEuWLMGnn34KRVECz2/lypWT/mcwIQ5u9u8W\nPHbsGBRFwRNPPIGpU6fGeliqUF1djQceeACvvvoqKioq8O///u/weDwoLi7Gxo0bodFoYj3EuLVx\n40a8/fbbKC4uDnzte9/7HjZu3MhnOE7d3d149NFH0dTUBK/Xi69//euYOnUqfw5DcMstt+Cxxx6D\nKIp8fkFwu9149NFHUVNTA0EQ8NBDD0EURTzxxBOQJAmrV6/G/fffH+thxrWf/exn2LlzJxRFwf33\n34+8vLxJ/zOYEOGKiIiIKF4kxLIgERERUbxguCIiIiKKIIYrIiIioghiuCIiIiKKIIYrIiIioghi\nuCIiIiKKIIYrIiIioghiuCIiIiKKoP8fMxcuFkreou0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed62198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 70000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This last prediction example is interesting - the model clearly understands the recurring pattern in the series well, but struggles to properly capture the downward trend that's in place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
